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PurposeThe primary purpose of this study is to examine how fan attendance at team special events and player appearances impact fan consumption (as measured by merchandise sales). Insights obtained could shed light on opportunities for professional soccer teams to expand revenues through enhanced fan consumption of goods and services.Design/methodology/approachA sample of 499 season ticket holders were used to assess fan consumption by measuring merchandise sales. Ordinary least squares (OLS) regressions were run for merchandise sales as the dependent variable of fan consumption. The control variables were age, gender (male = 1, 0 otherwise), and whether an individual has children.FindingsThe key independent variables of attending special events and fan–player bonding were both found to have a statistically significant impact on merchandise sales. Results show that each additional special event attended generates up to $33.71 in merchandise sales for the club. Similarly, each fan–player bonding experience attended also has a direct impact, increasing merchandise consumption by $23.00.Social implicationsThe results of this study provide insights that can help fan consumption grow within the professional United States soccer industry and better allow team managers to make decisions about the possible benefits of holding more special events and fan–player bonding experiences. The findings also confirm the impact personal relationships with fans can have on the bottom line of sport franchises.Originality/valueThough this study adds to the body of literature by expanding previous work on fan consumption, there are limited studies on the social aspects of consumption which are examined and analyzed within this study, particularly of note is the study of merchandise sales as proxy for fan consumption.

Objective Throughout the COVID-19 pandemic, there have been concerns about the mental health of health care workers (HCW). Although numerous studies have investigated the level of distress among HCW, few studies have explored programs to improve their mental well-being. In this paper, we describe the implementation and evaluation of a program to support the mental health of HCW at University Health Network (UHN), Canada's largest healthcare network. Methods Using a quality improvement approach, we conducted a needs assessment and then created and evaluated a modified stepped-care model to address HCW mental health during the pandemic. This included: online resources focused on psychoeducation and self-management, access to online support and psychotherapeutic groups, and self-referral for individual care from a psychologist or psychiatrist. We used ongoing mixed-methods evaluation, combining quantitative and qualitative analysis, to improve program quality. Results The program is ongoing, running continuously throughout the pandemic. We present data up to November 30, 2021. There were over 12,000 hits to the UHN's COVID mental health intranet web page, which included self-management resources and information on group support. One hundred and sixty-six people self-referred for individual psychological or psychiatric care. The mean wait time from referral to initial appointment was 5.4 days, with an average of seven appointments for each service user. The majority had moderate to severe symptoms of depression and anxiety at referral, with over 20% expressing thoughts of self-harm or suicide. Post-care user feedback, collected through self-report surveys and semistructured interviews, indicated that the program is effective and valued. Conclusions Development of a high-quality internal mental health support for HCW program is feasible, effective, and highly valued. By using early and frequent feedback from multiple perspectives and stakeholders to address demand and implement changes responsively, the program was adjusted to meet HCW mental health needs as the pandemic evolved.

Background Delirium is an acute neurocognitive disorder that affects up to half of older hospitalized medical patients and can lead to dementia, longer hospital stays, increased health costs, and death. Although delirium can be prevented and treated, it is difficult to identify and predict. Objective This study aimed to improve machine learning models that retrospectively identify the presence of delirium during hospital stays (eg, to measure the effectiveness of delirium prevention interventions) by using the natural language processing (NLP) technique of sentiment analysis (in this case a feature that identifies sentiment toward, or away from, a delirium diagnosis). Methods Using data from the General Medicine Inpatient Initiative, a Canadian hospital data and analytics network, a detailed manual review of medical records was conducted from nearly 4000 admissions at 6 Toronto area hospitals. Furthermore, 25.74% (994/3862) of the eligible hospital admissions were labeled as having delirium. Using the data set collected from this study, we developed machine learning models with, and without, the benefit of NLP methods applied to diagnostic imaging reports, and we asked the question “can NLP improve machine learning identification of delirium?” Results Among the eligible 3862 hospital admissions, 994 (25.74%) admissions were labeled as having delirium. Identification and calibration of the models were satisfactory. The accuracy and area under the receiver operating characteristic curve of the main model with NLP in the independent testing data set were 0.807 and 0.930, respectively. The accuracy and area under the receiver operating characteristic curve of the main model without NLP in the independent testing data set were 0.811 and 0.869, respectively. Model performance was also found to be stable over the 5-year period used in the experiment, with identification for a likely future holdout test set being no worse than identification for retrospective holdout test sets. Conclusions Our machine learning model that included NLP (ie, sentiment analysis in medical image description text mining) produced valid identification of delirium with the sentiment analysis, providing significant additional benefit over the model without NLP.

Introduction: Schizophrenia is associated with stroke incidence and case-fatality but the causes of this are not well-understood. We evaluated the association between comorbid schizophrenia and the quality of secondary prevention care after ischemic stroke. Methods: We used linked health administrative data to identify adults discharged alive from acute care hospitals in Ontario, Canada between 2004 and 2017 following an incident ischemic stroke and identified those with a history of schizophrenia using a validated algorithm. Outcomes were screening, treatment among those with the risk factor, control of vascular risk factors, and receipt of outpatient physician services. We used modified Poisson regression to model the relative risk of each indicator outcome among persons with and without schizophrenia who were alive one year after discharge, adjusting for age, sex, income, comorbidity and rural residence. Results: Among 85,046 persons with ischemic stroke, 886 (1.04%) had a diagnosis of schizophrenia. Those with schizophrenia were younger (median age 64 vs 72), more likely to be female (50.1% vs 44.6%), live in the lowest neighbourhood income quintile (41.1% vs 23.0%) and have diabetes (42.8% vs 32.3%). Of those alive 1 year after stroke, those with schizophrenia were less likely to be screened for hyperlipidemia (adjusted relative risk (aRR) 0.89; 95% confidence interval (CI) 0.84 to 0.94) or diabetes (aRR 0.93; 95% CI 0.89 to 0.97), be prescribed antihypertensive agents (aRR 0.96; 95% CI 0.93 to 0.99), or achieve target lipid levels (LDL < 2 mml/L) (aRR 0.87; 95% CI 0.78 to 0.96). There were no differences in prescription of antilipemic (aRR 0.96; 95% CI 0.91 to 1.01) or antiglycemic (aRR 0.95; 95% CI 0.87 to 1.03) agents or in achievement of target HbA1c ≤ 7% (aRR 0.89; 95% CI 0.78 to 1.02). Outpatient stroke specialist care in the 3 months after discharge was less likely among those with schizophrenia (aRR 0.72; 95% CI 0.65 to 0.78) as were visits to primary care physicians (aRR 0.93; 95% CI 0.91 to 0.96). Conclusions: People with schizophrenia and stroke are less likely to receive secondary preventive care across many indicators measured. This information may be useful for targeted interventions to improve post-stroke care in those with schizophrenia.

In this narrative review, we describe what is known about non-pharmacological and pharmacological treatments for insomnia in medical inpatients, with a focus on melatonin. Hospital-acquired insomnia is common, resulting in shortened total sleep time and more nighttime awakenings. Sleep disturbance has been shown to increase systemic inflammation, pain, and the likelihood of developing delirium in hospital. Treatment for insomnia includes both non-pharmacological and pharmacological interventions, the latter of which requires careful consideration of risks and benefits given the known adverse effects. Though benzodiazepines and non-benzodiazepine benzodiazepine receptor agonists are commonly prescribed (i.e., sedative-hypnotics), they are relatively contraindicated for patients over the age of 65 due to the risk of increased falls, cognitive decline, and potential for withdrawal symptoms after long-term use. Exogenous melatonin has a comparatively low likelihood of adverse effects and drug–drug interactions and is at least as effective as other sedative-hypnotics. Though more research is needed on both its effectiveness and relative safety for inpatients, small doses of melatonin before bedtime may be an appropriate choice for inpatients when insomnia persists despite non-pharmacological interventions.