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Abstract Background Physical activity and sleep are established modifiable lifestyle factors, but the optimal time of the day of these behaviours for health is unknown. This study examined the independent and joint associations of diurnal patterns of physical activity and sleep with all-cause mortality. Methods This prospective cohort study included 6,673 participants who have attended the accelerometer assessment in the 2011–2014 National Health and Nutrition Examination Surveys (NHANES). Diurnal patterns of accelerometer-measured physical activity and sleep were identified using K-means clustering analysis. All-cause mortality was ascertained from the accelerometer measurement to December 31, 2019 (median follow-up 6.8 years). Survey-weighted Cox proportional hazard models were performed to estimate the independent and joint associations of diurnal patterns of physical activity and sleep with all-cause mortality. Results Diurnal patterns identified were: early-morning (32.4%), midday (42.5%), and late-afternoon (25.1%) for physical activity; and irregular sleep (37.4%), morning lark (33.6%), and night owl (29.0%) for sleep. After adjusting for volume of physical activity, sleep duration and other potential covariates, the early-morning physical activity pattern (hazard ratio 1.36, 95% confidence interval 1.13–1.64) and irregular sleep pattern (1.42, 1.01–1.99) were independently associated with higher risk of all-cause mortality, compared with midday physical activity and morning lark sleep patterns, respectively. In addition, participants with the combined pattern of early-morning physical activity and irregular sleep had higher risk of all-cause mortality compared to those with midday physical activity combined with a morning lark sleep pattern (1.92, 1.33–2.78). Several sociodemographic differences were observed in the strength of these associations. Conclusions Wearable activity-rest monitoring data showed that peak physical activity in the early morning and irregular sleep diurnal patterns are associated with increased mortality risk, and the combination of these patterns further exaggerated the risk. Public health program should acknowledge that the diurnal patterns of physical activity and sleep, in addition to their duration and frequency, may play a crucial role in lifestyle-based health promotion and management strategies.

Aerosol science is of utmost importance for both climate and public health research, and in recent years X-ray techniques have proven effective tools for aerosol-particle characterization. To date, such methods have often involved the study of particles collected onto a substrate, but a high photon flux may cause radiation damage to such deposited particles and volatile components can potentially react with the surrounding environment after sampling. These and many other factors make studies on collected aerosol particles challenging. Therefore, a new aerosol sample-delivery system dedicated to X-ray photoelectron spectroscopy studies of aerosol particles and gas molecules in-flight has been developed at the MAX IV Laboratory. The aerosol particles are brought from atmospheric pressure to vacuum in a continuous flow, ensuring that the sample is constantly renewed, thus avoiding radiation damage, and allowing measurements on the true unsupported aerosol. At the same time, available gas molecules can be used for energy calibration and to study gas-particle partitioning. The design features of the aerosol sample-delivery system and important information on the operation procedures are described in detail here. Furthermore, to demonstrate the experimental range of the aerosol sample-delivery system, results from aerosol particles of different shape, size and composition are presented, including inorganic atmospheric aerosols, secondary organic aerosols and engineered nanoparticles.

OBJECTIVE: Leisure-time physical activity (PA) has been hypothesized to reduce the likelihood of occupational injuries, but it is unclear whether this association varies between workplace and commuting injuries. The aim of this study was to examine the association between PA and risk of workplace and commuting injuries. METHODS: Data were derived from the Finnish Public Sector study including 82 716 person-observations (48 116 participants). PA was requested repeatedly in four questionnaire surveys between 2000–2012. The average level of PA from two subsequent questionnaires was used to assess long-term PA. To obtain a 1-year incidence of injuries, participants were linked to occupational injury records from the national register. Logistic regression analysis with generalized estimating equations was used to examine the association between PA and injury risk. The analysis was adjusted for age, sex, education, work schedule, job demand, sleep difficulties, cardiovascular diseases, diabetes, and depression for workplace and commuting injuries, and workplace injuries were additionally adjusted for physical heaviness of an occupation and injury risk by occupation. RESULTS: Higher level of PA was associated with a lower risk of workplace injuries compared to inactive participants [odds ratio (OR) 0.85, 95% confidence interval (CI) 0.73–0.98]. This association was most marked in the ≥50-year-old age group (OR 0.78, 95% CI 0.64–0.99). No association between the PA and the risk for commuting injuries was observed. CONCLUSION: Higher PA is associated with lower risk of workplace injuries particularly among older employees.

Engaging parents in school health examinations can promote adolescents' well-being. We examined parents' participation in universal school health examinations in Finland reported by adolescents in school surveys (14 to 16-year-olds, N = 58,232). Further we studied variation between service providers and schools, and student and school-level factors in participation. National data were analyzed using multilevel logistic regression models. Less than half of the adolescents reported parents' participation. The variation between service providers and schools was large. Non-participation was associated with mother's low education, students' immigrant background, daily health complaints, heavy drinking, and discussion difficulties with parents. Boys and those who did not live with both mother and father had a higher risk for parents' non-participation. Adolescents with a long-term illness or being bullied reported participation more often. Inviting parents and the school health nurse resource were not associated with participation. Our results raise the question of barriers to participation in health examinations.

Parents` significance in early childhood education and care (ECEC) is emphasized in the research, but primarily from the perspective of ECEC professionals. Drawing on discursive institutionalism, we analysed what parents of young children in Finland constructed as essential in ECEC from the child`s point of view in their discussions concerning the forms of ECEC services. We found that parents constructed ECEC through two interconnected frames: enrichment and safety. The frames indicate that an individual child and her well-being here and now are considered essential in ECEC for Finnish parents. Parents' interpretations differ from one of the global discourses of ECEC, which emphasizes children's development for the future. On the other hand, parents' discourses maintain the cultural distinctions traditionally present in the Finnish ECEC institution. Our study underscores the significance of scrutinizing the construction of educational institutions in the discourses of those whose everyday lives these institutions are.

Kolumnissaan ”Epäonnistunut sotahistoria” (Ennen ja nyt 4/2023) historiantutkija Jussi Jalonen kritisoi niin sanottua uutta sotahistoriaa siitä, ettei se ole viimeisen 20 vuoden aikana onnistunut tuomaan keskusteluun riittävän laaja-alaista ymmärrystä sodan yhteiskunnallisista ja taloudellisista realiteeteista. Tämän seurauksena nykyistä sotaa Ukrainas-sa tarkastellaan Suomessa kapeasti yksittäisten sotatoimien ja asejärjestelmien kautta. Jalonen antaa tutkijoille synninpäästön ja näkee syynä tilanteeseen kaupallisen median, jonka ”sotaextroilla” on ollut tutkimusta suurempi rooli lattean sotakäsityksen luomisessa. Tästä huolimatta Jalosen puheenvuoro on hyvä ärsyke myös tutkimukselle. Onko sodan sosiaali- ja kulttuurihistoria epäonnistunut tehtävässään laajentaa ja syventää käsitystä sodasta ilmiönä? Mistä tutkimuksen painopisteet kertovat ja mihin uusiin suuntiin sodan sosiaali- ja kulttuurihistorian kannattaisi edetä? Miksi historiallinen ymmärrys sodasta on tärkeää?

Identifying circulating proteins associated with cognitive function may point to biomarkers and molecular process of cognitive impairment. Few studies have investigated the association between circulating proteins and cognitive function. We identify 246 protein measures quantified by the SomaScan assay as associated with cognitive function (p

Summary: Background: While individuals who were separated from their biological family and placed into the care of the state during childhood (out-of-home care) are more prone to developing selected adverse health problems in adulthood, their risk of cardiovascular disease is uncertain. Our aim was to explore this association by pooling published and unpublished results from prospective cohort studies. Methods: We used two approaches to identifying relevant data on childhood care and adult cardiovascular disease (PROSPERO registration CRD42021254665). First, to locate published studies, we searched PubMed (Medline) until November 2023. Second, with the objective of identifying unpublished studies with the potential to address the present research question, we scrutinised retrieved reviews on childhood out-of-home care and other adult health outcomes. Included studies were required to satisfy three criteria: a cohort study in which the assessment of care was made prospectively pre-adulthood (in the avoidance of recall bias); data on an unexposed comparator group were available (for the computation of relative risk); and a diagnosis of adult cardiovascular disease events (coronary heart disease, stroke, or their combination) had been made (as opposed to risk factors only). Collaborating investigators provided study-specific estimates which were aggregated using random-effects meta-analysis. The Newcastle-Ottawa Scale was used to assess individual study quality. Findings: Twelve studies (2 published, 10 unpublished) met the inclusion criteria, and investigators from nine provided viable results, including updated analyses of the published studies. Studies comprised 611,601 individuals (301,129 women) from the US, UK, Sweden, Finland, and Australia. Five of the nine studies were judged to be of higher methodological quality. Relative to the unexposed, individuals with a care placement during childhood had a 51% greater risk of cardiovascular disease in adulthood (summary rate ratio after age- and sex-adjustment [95% confidence interval]: 1.51 [1.22, 1.86]; range of study-specific estimates: 1.07 to 2.06; I2 = 69%, p = 0.001). This association was attenuated but persisted after adjustment for socioeconomic status in childhood (8 studies; 1.41 [1.15, 1.72]) and adulthood (9 studies, 1.29 [1.11, 1.51]). Interpretation: Our findings show that individuals with experience of out-of-home care in childhood have a moderately raised risk of cardiovascular disease in adulthood. Funding: Medical Research Council; National Institute on Aging; Wellcome Trust.

Introduction Adolescence is a sensitive period for cardiometabolic health. Yet, it remains unknown if adolescent health behaviours, such as alcohol use, smoking, diet and physical activity, have differential effects across socioeconomic strata. Adopting a life-course perspective and a causal inference framework, we aim to assess whether the effects of adolescent health behaviours on adult cardiometabolic health differ by levels of neighbourhood deprivation, parental education and occupational class. Gaining a better understanding of these social disparities in susceptibility to health behaviours can inform policy initiatives that aim to improve population health and reduce socioeconomic inequalities in cardiometabolic health.Methods and analysis We will conduct a secondary analysis of the Young Finns Study, which is a longitudinal population-based cohort study. We will use measures of health behaviours—smoking, alcohol use, fruit and vegetable consumption, and physical activity—as exposure and parental education, occupational class and neighbourhood deprivation as effect modifiers during adolescence (ages 12–18 years). Eight biomarkers of cardiometabolic health (outcomes)—waist circumference, body mass index, blood pressure, low-density lipoprotein cholesterol, apolipoprotein B, plasma glucose and insulin resistance—will be measured when participants were aged 33–40. A descriptive analysis will investigate the clustering of health behaviours. Informed by this, we will conduct a causal analysis to estimate effects of single or clustered adolescent health behaviours on cardiometabolic health conditional on socioeconomic background. This analysis will be based on a causal model implemented via a directed acyclic graph and inverse probability-weighted marginal structural models to estimate effect modification.Ethics and dissemination The Young Finns study was conducted according to the guidelines of the Declaration of Helsinki, and the protocol was approved by ethics committees of University of Helsinki, Kuopio, Oulu, Tampere and Turku. We will disseminate findings at international conferences and a manuscript in an open-access peer-reviewed journal.

Summary: Background: Physical abuse can lead to severe health consequences that extend beyond immediate harm. We explored the associations of physical abuse experienced during childhood and adulthood with a wide range of adult health conditions requiring hospital treatment. Methods: We utilised data from a sub-cohort of 157,366 UK Biobank participants (46.4% of the baseline population; age range 45–81; 89,101 women) and repeated analyses in an independent population of 85,929 adults from the Finnish Public Sector (FPS) study (age range 17–78; 68,544 women). Participants in both cohorts reported instances of physical and sexual abuse at study baseline. Follow-up included 77 common health conditions ascertained from linkage data to national hospital and mortality registries. Findings: Mean follow-up duration was 4.6 years (SD 0.14) in UK Biobank and 10.6 years (4.3) in FPS. Physical and sexual abuse was associated with 22 mental and physical health conditions. After multivariable adjustments, participants who experienced abuse during both early and later stages of life had a 2.12- (95% confidence interval 1.39–3.23) to 3.37-fold (1.52–7.45) increased risk of mental and behavioural disorders, a 1.46 (1.20–1.79) to 1.83 (1.05–3.20) times increased risk of metabolic, haematologic, and respiratory diseases, and a 1.24 (1.07–1.45) times higher risk of inflammatory diseases compared with non-exposed participants. The absolute risk difference between these groups was greatest for metabolic and haematologic conditions (rate 381 and risk difference 160 per 100,000 person-years). Frailty, comorbidities, and competing risk of death did not modify these associations, but the possibility of bias or residual confounding cannot be excluded. Interpretation: Repeated exposure to physical and sexual abuse amplifies the risk of hospitalisations from mental disorders and physical diseases spanning diverse organ systems. Addressing this issue may necessitate multifaceted strategies, including shifts in societal norms, legal measures, and increased healthcare provision for affected individuals and their families. Funding: Wellcome Trust, UK Medical Research Council, U.S. National Institute on Aging, Academy of Finland.

Objectives To investigate if the Jenkins Sleep Scale (JSS) demonstrates sex-related differential item functioning (DIF).Design Cross-sectional study.Setting Survey data from the Finnish Public Sector study (2015–2017).Participants 77 967 employees in the Finnish public sector, with a mean age of 51.9 (SD 13.1) years and 82% women.Outcome measures Item response theory estimates: difficulty and discrimination parameters of the JSS and differences in these parameters between men and women.Results The mean JSS total score was 6.4 (4.8) points. For all four items of the JSS, the difficulty parameter demonstrated a slight shift towards underestimation of the severity of sleep difficulties. The discrimination ability of all four items was moderate to high. For the JSS composite score, overall discrimination ability was moderate (0.98, 95% CI 0.97 to 0.99). Mild uniform DIF (p

Abstract Identifying circulating proteins associated with cognitive function may point to biomarkers and molecular process of cognitive impairment. Few studies have investigated the association between circulating proteins and cognitive function. We identify 246 protein measures quantified by the SomaScan assay as associated with cognitive function (p

OBJECTIVE: This study aimed to compare the utility of risk estimation derived from questionnaires and administrative records in predicting long-term sickness absence among shift workers.METHODS: This prospective cohort study comprised 3197 shift-working hospital employees (mean age 44.5 years, 88.0% women) who responded to a brief 8-item questionnaire on work disability risk factors and were linked to 28 variables on their working hour and workplace characteristics obtained from administrative registries at study baseline. The primary outcome was the first sickness absence lasting ≥90 days during a 4-year follow-up.RESULTS: The C-index of 0.73 [95% confidence interval (CI) 0.70–0.77] for a questionnaire-only based prediction model, 0.71 (95% CI 0.67–0.75) for an administrative records-only model, and 0.79 (95% CI 0.76–0.82) for a model combining variables from both data sources indicated good discriminatory ability. For a 5%-estimated risk as a threshold for positive test results, the detection rates were 76%, 74%, and 75% and the false positive rates were 40%, 45% and 34% for the three models. For a 20%-risk threshold, the corresponding detection rates were 14%, 8%, and 27% and the false positive rates were 2%, 2%, and 4%. To detect one true positive case with these models, the number of false positive cases accompanied varied between 7 and 10 using the 5%-estimated risk, and between 2 and 3 using the 20%-estimated risk cut-off. The pattern of results was similar using 30-day sickness absence as the outcome.CONCLUSIONS: The best predictive performance was reached with a model including both questionnaire responses and administrative records. Prediction was almost as accurate with models using only variables from one of these data sources. Further research is needed to examine the generalizability of these findings.

Abstract Psychosocial work environment characteristics like job demands have traditionally been studied using survey data. We propose an alternative approach utilizing work related trace data collected from the information systems that employees use to achieve organizational goals. We analyze the job demands of teachers from two universities of applied sciences using trace data collected from the educational online platform Moodle over a period of 90 weeks. The data contain pairs of targets and actions (like message_sent) performed by teachers on Moodle. The timestamps of the target-action pairs allow us to study the dynamic nature of job demands, which is not possible by using periodically collected survey data. We show how trace data can be used to analyze processes related to job demands using data-driven approaches. We have identified topics, themes, temporal processes, and employee clusters from Moodle data representing the work tasks of teachers. The information obtained is action-oriented, context-specific, and dynamic, meeting the current needs for information about changing working life. The approach we have provided could be widely utilized in organizations as well as in research on occupational wellbeing. It is useful in identifying targets for intervention and it could be expanded to include prediction models on different outcomes.

The Arctic and boreal regions are experiencing a rapid increase in temperature, resulting in a changing cryosphere, increasing human activity, and potentially increasing high-latitude methane emissions. Satellite observations from Sentinel-5P TROPOMI provide an unprecedented coverage of a column-averaged dry-air mole fraction of methane (XCH4) in the Arctic, compared to previous missions or in situ measurements. The purpose of this study is to support and enhance the data used for high-latitude research through presenting a systematic evaluation of TROPOMI methane products derived from two different processing algorithms: the operational product (OPER) and the scientific product (WFMD), including the comparison of recent version changes of the products (OPER, OPER rpro, WFMD v1.2, and WFMD v1.8). One finding is that OPER rpro yields lower XCH4 than WFMD v1.8, the difference increasing towards the highest latitudes. TROPOMI product differences were evaluated with respect to ground-based high-latitude references, including four Fourier Transform Spectrometer in the Total Carbon Column Observing Network (TCCON) and five EM27/SUN instruments in the Collaborative Carbon Column Observing Network (COCCON). The mean TROPOMI–TCCON GGG2020 daily median XCH4 difference was site-dependent and varied for OPER rpro from −0.47 ppb to 22.4 ppb, and for WFMD v1.8 from 1.2 ppb to 19.4 ppb with standard deviations between 13.0 and 20.4 ppb and 12.5–15.0 ppb, respectively. The TROPOMI–COCCON daily median XCH4 difference varied from −26.5 ppb to 5.6 ppb for OPER rpro, with a standard deviation of 14.0–28.7 ppb, and from −5.0 ppb to 17.2 ppb for WFMD v1.8, with a standard deviation of 11.5–13.0 ppb. Although the accuracy and precision of both TROPOMI products are, on average, good compared to the TCCON and COCCON, a persistent seasonal bias in TROPOMI XCH4 (high values in spring; low values in autumn) is found for OPER rpro and is reflected in the higher standard deviation values. A systematic decrease of about 7 ppb was found between TCCON GGG2014 and GGG2020 product update highlighting the importance of also ensuring the reliability of ground-based retrievals. Comparisons to atmospheric profile measurements with AirCore carried out in Sodankylä, Northern Finland, resulted in XCH4 differences comparable to or smaller than those from ground-based remote sensing.

The collection and selection of the data used in learning analytics applications deserve more attention. Optimally, selection of data should be guided by pedagogical purposes instead of data availability. Using design science research methodology, we designed an artifact to collect time-series data on students' self-regulated learning and conceptual thinking. Our artifact combines curriculum data, concept mapping, and structured learning diaries. We evaluated the artifact in a case study, verifying that it provides relevant data, requires a limited amount of effort from students, and works in different educational contexts. Combined with learning analytics applications and interventions, our artifact provides possibilities to add value for students, teachers, and academic leaders.

Purpose: Individual socioeconomic status is associated with increased arterial stiffness, but limited data are available on the relations of neighbourhood deprivation with this vascular measure. We prospectively examined whether neighbourhood deprivation in childhood and adulthood predicts arterial stiffness indicated by pulse wave velocity (PWV). Materials and methods: The study population comprised 1,761 participants aged 3-18 years at baseline (1980) from the longitudinal Cardiovascular Risk in Young Finns cohort study. PWV was measured in 2007 by whole-body impedance cardiography at ages 30-45 years. Cumulative lifetime neighbourhood deprivation was assessed using data from socioeconomic circumstances in participants’ lifetime residential neighbourhoods, categorised as low versus high deprivation. Results: High deprivation in childhood and adulthood was associated with higher PWV in adulthood after adjustment for age, sex, and place of birth (mean difference = 0.57 m/s, 95%CI = 0.26-0.88, P for trend = 0.0004). This association was attenuated but remained statistically significant after further adjustment for childhood parental socioeconomic status and adulthood individual socioeconomic status (mean difference = 0.37 m/s, 95%CI = 0.05-0.70, P for trend 0.048). Also, low individual socioeconomic status in adulthood was associated with higher PWV when adjusted for age, sex, place of birth, parental socioeconomic status in childhood, and lifetime neighbourhood deprivation (mean difference = 0.54 m/s, 95%CI = 0.23-0.84, P for trend 0.0001). Conclusion: These findings suggest that lifetime neighbourhood deprivation and low adulthood socioeconomic status are independent risk factors for increased arterial stiffness in adulthood.

Background: Sexual and physical abuse have been associated with long-term systemic alterations such as low-grade inflammation and changes in brain morphology that may be reflected in the metabolome. However, data on the metabolic consequences of sexual and physical abuse remain scarce. Objective: This pilot study sought to investigate changes in the metabolite profile related to sexual and physical abuse in depressed adolescent psychiatric outpatients. Method: The study included 76 patients aged 14–18 years, whose serum samples were analysed with a targeted metabolite profiling methodology. We estimated the associations between metabolite concentrations and the Trauma and Distress Scale (TADS) Sexual and Physical Abuse factor scores using three linear regression models (one unadjusted and two adjusted) per metabolite and trauma type pair. Additional variables in the two adjusted models were 1) the lifestyle indicators body mass index, tobacco use, and alcohol use, and 2) depression scores and the chronicity of depression. Results: TADS Sexual Abuse scores associated positively with homogentisic acid, as well as cystathionine, and negatively with choline in linear regression analysis, whereas TADS Physical Abuse scores associated negatively with AMP, choline, γ-glutamyl cysteine and succinate, and positively with D-glucuronic acid. Conclusions: This pilot study did not include a healthy control group for comparison and the cohort was relatively small. Nevertheless, we observed alterations in metabolites related to one-carbon metabolism, mitochondrial dysfunction, oxidative stress, and inflammation in depressed patients with a history of sexual or physical abuse.

Abstract Few risk prediction scores are available to identify people at increased risk of work disability, particularly for those with an existing morbidity. We examined the predictive performance of disability risk scores for employees with chronic disease. We used prospective data from 88,521 employed participants (mean age 43.1) in the Finnish Public Sector Study including people with chronic disorders: musculoskeletal disorder, depression, migraine, respiratory disease, hypertension, cancer, coronary heart disease, diabetes, comorbid depression and cardiometabolic disease. A total of 105 predictors were assessed at baseline. During a mean follow-up of 8.6 years, 6836 (7.7%) participants were granted a disability pension. C-statistics for the 8-item Finnish Institute of Occupational Health (FIOH) risk score, comprising age, self-rated health, number of sickness absences, socioeconomic position, number of chronic illnesses, sleep problems, BMI, and smoking at baseline, exceeded 0.72 for all disease groups and was 0.80 (95% CI 0.80–0.81) for participants with musculoskeletal disorders, 0.83 (0.82–0.84) for those with migraine, and 0.82 (0.81–0.83) for individuals with respiratory disease. Predictive performance was not significantly improved in models with re-estimated coefficients or a new set of predictors. These findings suggest that the 8-item FIOH work disability risk score may serve as a scalable screening tool in identifying individuals with increased risk for work disability.

Background Current dementia risk scores have had limited success in consistently identifying at-risk individuals across different ages and geographical locations.Objective We aimed to develop and validate a novel dementia risk score for a midlife UK population, using two cohorts: the UK Biobank, and UK Whitehall II study.Methods We divided the UK Biobank cohort into a training (n=176 611, 80%) and test sample (n=44 151, 20%) and used the Whitehall II cohort (n=2934) for external validation. We used the Cox LASSO regression to select the strongest predictors of incident dementia from 28 candidate predictors and then developed the risk score using competing risk regression.Findings Our risk score, termed the UK Biobank Dementia Risk Score (UKBDRS), consisted of age, education, parental history of dementia, material deprivation, a history of diabetes, stroke, depression, hypertension, high cholesterol, household occupancy, and sex. The score had a strong discrimination accuracy in the UK Biobank test sample (area under the curve (AUC) 0.8, 95% CI 0.78 to 0.82) and in the Whitehall cohort (AUC 0.77, 95% CI 0.72 to 0.81). The UKBDRS also significantly outperformed three other widely used dementia risk scores originally developed in cohorts in Australia (the Australian National University Alzheimer’s Disease Risk Index), Finland (the Cardiovascular Risk Factors, Ageing, and Dementia score), and the UK (Dementia Risk Score).Clinical implications Our risk score represents an easy-to-use tool to identify individuals at risk for dementia in the UK. Further research is required to determine the validity of this score in other populations.

Objectives The COVID-19 pandemic has globally affected healthcare workers’ (HCWs) health and wellbeing. Most studies on COVID-19 have focused on tertiary healthcare. The aim of this study was to increase the knowledge on the effects of the pandemic on working conditions in tertiary and primary healthcare. Material and Methods The comparative cross-sectional study consisted of an online questionnaire sent to HCWs of the City of Helsinki (primary healthcare) and Helsinki University Hospital (tertiary healthcare). Altogether 1580 HCWs with direct patient contact participated in the study: 895 from tertiary and 685 from primary healthcare. Statistical analysis used SPSS 25 from IBM. The tests used were the χ 2 test, Fisher’s exact test, and binary logistic regression analysis. Results Primary HCWs were less likely to treat COVID-19 patients (OR = 0.45, 95% CI: 0.37–0.56). However, both groups reported a similar number of COVID-19 infections, primary HCWs 4.9% and tertiary HCWs 5.0%, and workrelated quarantine was significantly more prevalent (OR = 1.96, 95% CI: 1.38–2.79) among primary HCWs. In addition, work-related wellbeing was poorer among primary HCWs than tertiary HCWs in terms of feeling more stressed at work (OR = 3.20, 95% CI: 2.55–4.02), not recovering from work (OR = 0.49, 95% CI: 0.39–0.62), reported mental wellbeing below normal levels (OR: 1.59, 95% CI: 1.26–2.00), and increased working hours (OR = 1.63, 95% CI: 1.25–2.12). Conclusions The study demonstrates how the pandemic has affected the wellbeing and working conditions of not only tertiary but also less studied primary HCWs. The authors’ findings suggest that the challenges identified during the COVID-19 pandemic in the health and wellbeing of healthcare workers are even greater in primary care than in tertiary care. Int J Occup Med Environ Health. 2023;36(1):139–50

Abstract Increasingly precise spatial data (e.g. high‐resolution imagery from remote sensing) allow for improved representations of the landscape network for assessing the combined effects of habitat loss and connectivity declines on biodiversity. However, evaluating large landscape networks presents a major computational challenge both in terms of working memory and computation time. We present the ConScape (i.e. “connected landscapes”) software library implemented in the high‐performance open‐source Julia language to compute metrics for connected habitat and movement flow on high‐resolution landscapes. The combination of Julia's ‘just‐in‐time’ compiler, efficient algorithms and ‘landmarks’ to reduce the computational load allows ConScape to compute landscape ecological metrics—originally developed in metapopulation ecology (such as ‘metapopulation capacity’ and ‘probability of connectivity’)—for large landscapes. An additional major innovation in ConScape is the adoption of the randomized shortest paths framework to represent connectivity along the continuum from optimal to random movements, instead of only those extremes. We demonstrate ConScape's potential for using large datasets in sustainable land planning by modelling landscape connectivity based on remote‐sensing data paired with GPS tracking of wild reindeer in Norway. To guide users, we discuss other applications, and provide a series of worked examples to showcase all ConScape's functionalities in Supplementary Material. Built by a team of ecologists, network scientists and software developers, ConScape is able to efficiently compute landscape metrics for high‐resolution landscape representations to leverage the availability of large data for sustainable land use and biodiversity conservation. As a Julia implementation, ConScape combines computational efficiency with a transparent code base, which facilitates continued innovation through contributions from the rapidly growing community of landscape and connectivity modellers using Julia.

OBJECTIVE: In terms of prevention, it is important to determine effects on cardiovascular disease (CVD) when some workplace psychosocial resources are high while others are low. The aim of the study was to assess the prospective relationship between clustering of workplace psychosocial resources and risk of CVD among employees. METHODS: We pooled data from three cohort studies of 135 669 employees (65% women, age 18–65 years and free of CVD) from Denmark, Finland and Sweden. Baseline horizontal resources (culture of collaboration and support from colleagues) and vertical resources (leadership quality and procedural justice) were measured using standard questionnaire items. Incident CVD, including coronary heart and cerebrovascular disease, was ascertained using linked electronic health records. We used latent class analysis to assess clustering (latent classes) of workplace psychosocial resources. Cox proportional hazard models were used to examine the association between these clusters and risk of CVD, adjusting for demographic and employment-related factors and pre-existing physical and mental disorders. RESULTS: We identified five clusters of workplace psychosocial resources from low on both vertical and horizontal resources (13%) to generally high resources (28%). High horizontal resources were combined with either intermediate [hazard ratio (HR) 0.84, 95% confidence interval (CI) 0.74–0.95] or high (HR 0.88, 95% CI 0.78–1.00) vertical resources were associated with lower risks of CVD compared to those with generally low resources. The association was most prominent for cerebrovascular disease (eg, general high resources: HR 0.80, 95% CI 0.67–0.96). CONCLUSIONS: Individuals with high levels of workplace psychosocial resources across horizontal and vertical dimensions have a lower risk of CVD, particularly cerebrovascular disease.

Objectives To develop a risk prediction algorithm for identifying work units with increased risk of violence in the workplace.Design Prospective cohort study.Setting Public sector employees in Finland.Participants 18 540 nurses, social and youth workers, and teachers from 4276 work units who completed a survey on work characteristics, including prevalence and frequency of workplace violence/threat of violence at baseline in 2018–2019 and at follow-up in 2020–2021. Those who reported daily or weekly exposure to violence or threat of violence daily at baseline were excluded.Exposures Mean scores of responses to 87 survey items at baseline were calculated for each work unit, and those scores were then assigned to each employee within that work unit. The scores measured sociodemographic characteristics and work characteristics of the work unit.Primary outcome measure Increase in workplace violence between baseline and follow-up (0=no increase, 1=increase).Results A total of 7% (323/4487) of the registered nurses, 15% (457/3109) of the practical nurses, 5% of the social and youth workers (162/3442) and 5% of the teachers (360/7502) reported more frequent violence/threat of violence at follow-up than at baseline. The area under the curve values estimating the prediction accuracy of the prediction models were 0.72 for social and youth workers, 0.67 for nurses, and 0.63 for teachers. The risk prediction model for registered nurses included five work unit characteristics associated with more frequent violence at follow-up. The model for practical nurses included six characteristics, the model for social and youth workers seven characteristics and the model for teachers included four characteristics statistically significantly associated with higher likelihood of increased violence.Conclusions The generated risk prediction models identified employees working in work units with high likelihood of future workplace violence with reasonable accuracy. These survey-based algorithms can be used to target interventions to prevent workplace violence.

Summary: Background: Few large-scale studies have examined the health impacts of overcrowded housing in European countries. The aim of this study was to assess whether household crowding during adolescence increases the risk of all-cause and cause-specific mortality in Switzerland. Methods: Study participants were 556,191 adolescents aged 10–19 years at the 1990 census from the Swiss National Cohort. Household crowding at baseline was measured as the ratio between the number of persons living in the household and the number of available rooms, categorized as none (ratio ≤ 1), moderate (1 < ratio ≤ 1.5), and severe (ratio > 1.5). Participants were linked to administrative mortality records through 2018 and followed for premature mortality from all causes, cardiometabolic disease and self-harm or substance use. Cumulative risk differences between ages 10 and 45 were standardized by parental occupation, residential area, permit status and household type. Findings: Of the sample, 19% lived in moderately and 5% lived in severely crowded households. During an average follow-up of 23 years, 9766 participants died. Cumulative risk of death from all causes was 2359 (95% compatibility intervals: 2296–2415) per 100,000 persons when living in non-crowded households. Living in moderately crowded households led to 99 additional deaths (−63 to 256) per 100,000 persons and living in severely crowded households 258 additional deaths (−37 to 607) per 100,000 persons. The effect of crowding on mortality from cardiometabolic diseases, self-harm or substance use was negligible. Interpretation: Excess risk of premature mortality in adolescents living in overcrowded households appears to be small or negligible in Switzerland. Funding: University of Fribourg Scholarship Programme for foreign post-doctoral researchers.

IntroductionEpistemic cognition, which explores how knowledge is acquired, applied, and critically evaluated, is closely linked to learning. However, despite numerous studies from various perspectives, much remains to be learned about this essential and predominantly positive connection. Despite the positive connection between epistemic cognition and learning, epistemic measures have not been widely used in predicting students’ academic achievement. One possible reason for this is the difficulties in measuring personal epistemology. However, in the last decade, learning analytics has emerged as a field of study and practice with new means to collect data on different psychological constructs. This study focused on a learning analytics tool, a structured learning diary, that can support student learning while being used to record student thinking. This study explores the connection of student diaries with academic achievement, students’ epistemic beliefs’ connection with academic achievement, and the viability of implementing an epistemic belief questionnaire and a structured learning diary in a naturalistic degree program setting (N = 105).MethodsConnections between these and academic achievement were investigated at four temporal measurement points. The first aim was to test which measures of the diary tool correlated with academic achievement. The second aim was to test epistemic beliefs’ correlation (Pearson correlation coefficient) with academic achievement. Models of linear regression were then designed and tested at different times. The overarching general aim of the study was to fill the gap in the use of reflective learning diaries in engineering education and epistemic beliefs in predicting academic achievement.Results and discussionThe results show that we should collect student-originated learning process data for the best predictive power and connect that with independent psychological measures. Despite the significant effort required to use the learning diaries, the results indicate that with further design, digital journaling tools are viable learning and measurement tools to be used at scale and for long periods. Future studies should investigate the possibility implement epistemic measures with structured learning diaries more closely and study the connections between diary use and personal epistemology.

IntroductionNew digital tools such as structured learning diaries (SLD) can serve as both a measurement and intervention tool, at scale. However, we do not yet know how students use the SLD tool and justify their actions within it. Clickable items are easy to use, but do these diaries hold any deeper meaning for students? This study aims to explore the means of justification used by students to create SLD content, their profiles based on these justifications, their levels of reflection, and the relationship between the profiles and use of SLDs.MethodsWe interviewed a sample of first-year master’s-level engineering students to gather justifications and reflections related to the content of their SLDs. Rank- and median-based statistical tests were used to explore the connections between the interview-based profiles, and diary behavior was analyzed through log data.Results and DiscussionOur findings revealed distinctive profiles with different characteristics related to the structured learning diary behavior, including differences in how changes of difficulty and emotion ratings were made in SLDs. This study opens up a new area for future research and encourages the development of structured learning diary tools as a means of monitoring changes in student thinking at scale.

Remote sensing satellites provide a vast amount of data to monitor and observe Earth’s surface and events on it. To use these data efficiently in subsequent analysis and decision-making, highly automated easy-to-use tools are needed. Here, we present Earth Observation Data Information Extractor (EODIE). EODIE is a toolkit to extract object-level time-series information from several multispectral satellite remote sensing platforms and to produce analysis-ready products for subsequent data analysis. EODIE has a modular design that makes it adjustable for end-user requirements. Users have a possibility to exchange and add modules in EODIE for flexible processing in different computing environments. With EODIE, remote sensing data can be processed to object level array, geotiff or statistics information of different (vegetation) indices or plain wavelength intervals.

The randomized shortest paths (RSP) framework, developed for network analysis, extends traditional proximity and distance measures between two nodes, such as shortest path distance and commute cost distance (related to resistance distance). Consequently, the RSP framework has gained popularity in studies on landscape connectivity within ecology and conservation, where the behavior of animals is neither random nor optimal. In this work, we study how local perturbations in a network affect proximity and distance measures derived from the RSP framework. For this sensitivity analysis, we develop computable expressions for derivatives with respect to weights on the edges or nodes of the network. Interestingly, the sensitivity of expected cost to edge or node features provides a new signed network centrality measure, the negative covariance between edge/node visits and path cost, that can be used for pinpointing strong and weak parts of a network. It is also shown that this quantity can be interpreted as minus the endured expected detour (in terms of cost) when constraining the walk to pass through the node or the edge. Our demonstration of this framework focuses on a migration corridor for wild reindeer ( Rangifer rangifer ) in Southern Norway. By examining the sensitivity of the expected cost of movement between winter and calving ranges to perturbations in local areas, we have identified priority areas crucial for the conservation of this migration corridor. This innovative approach not only holds great promise for conservation and restoration of migration corridors, but also more generally for connectivity corridors between important areas for biodiversity (e.g. protected areas) and climate adaptation. Furthermore, the derivations and computational methods introduced in this work present fundamental features of the RSP framework. These contributions are expected to be of interest to practitioners applying the framework across various disciplines, ranging from ecology, transport and communication networks to machine learning.

Recent technological advancements in wearable sensors have made it easier to detect sweat components, but our limited understanding of sweat restricts its application. A critical bottleneck for temporal and regional sweat analysis is achieving uniform, high-throughput fabrication of sweat sensor components, including microfluidic chip and sensing electrodes. To overcome this challenge, we introduce microfluidic sensing patches mass fabricated via roll-to-roll (R2R) processes. The patch allows sweat capture within a spiral microfluidic for real-time measurement of sweat parameters including [Na+], [K+], [glucose], and sweat rate in exercise and chemically induced sweat. The patch is demonstrated for investigating regional sweat composition, predicting whole-body fluid/electrolyte loss during exercise, uncovering relationships between sweat metrics, and tracking glucose dynamics to explore sweat-to-blood correlations in healthy and diabetic individuals. By enabling a comprehensive sweat analysis, the presented device is a crucial tool for advancing sweat testing beyond the research stage for point-of-care medical and athletic applications.

Development of reliable glucose sensors for noninvasive monitoring without interruption or limiting users' mobility is highly desirable, especially for diabetes diagnostics, which requires routine/long-term monitoring. However, their applications are largely limited by the relatively poor stability. Herein, a porous membrane is synthesized for effective enzyme immobilization and it is robustly anchored to the modified nanotextured electrode solid contacts, so as to realize glucose sensors with significantly enhanced sensing stability and mechanical robustness. To the best of our knowledge, this is the first report of utilizing such nanoporous membranes for electrochemical sensor applications, which eliminates enzyme escape and provides a sufficient surface area for molecular/ion diffusion and interactions, thus ensuring the sustainable catalytic activities of the sensors and generating reliable measureable signals during noninvasive monitoring. The as-assembled nanostructured glucose sensors demonstrate reliable long-term stable monitoring with a minimal response drift for up to 20 h, which delivers a remarkable enhancement. Moreover, they can be integrated into a microfluidic sensing patch for noninvasive sweat glucose monitoring. The as-synthesized nanostructured glucose sensors with remarkable stability can inspire developments of various enzymatic biosensors for reliable noninvasive composition analysis and their ultimate applications in predictive clinical diagnostics, personalized health-care monitoring, and chronic diseases management.