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BackgroundThe Internet is used increasingly for both suicide research and prevention. To optimize online assessment of suicidal patients, there is a need for short, good-quality tools to assess elevated risk of future suicidal behavior. Computer adaptive testing (CAT) can be used to reduce response burden and improve accuracy, and make the available pencil-and-paper tools more appropriate for online administration. ObjectiveThe aim was to test whether an item response–based computer adaptive simulation can be used to reduce the length of the Beck Scale for Suicide Ideation (BSS). MethodsThe data used for our simulation was obtained from a large multicenter trial from The Netherlands: the Professionals in Training to STOP suicide (PITSTOP suicide) study. We applied a principal components analysis (PCA), confirmatory factor analysis (CFA), a graded response model (GRM), and simulated a CAT. ResultsThe scores of 505 patients were analyzed. Psychometric analyses showed the questionnaire to be unidimensional with good internal consistency. The computer adaptive simulation showed that for the estimation of elevation of risk of future suicidal behavior 4 items (instead of the full 19) were sufficient, on average. ConclusionsThis study demonstrated that CAT can be applied successfully to reduce the length of the Dutch version of the BSS. We argue that the use of CAT can improve the accuracy and the response burden when assessing the risk of future suicidal behavior online. Because CAT can be daunting for clinicians and applied scientists, we offer a concrete example of our computer adaptive simulation of the Dutch version of the BSS at the end of the paper.

Abstract Background In 2012, in The Netherlands a multidisciplinary practice guideline for the assessment and treatment of suicidal behavior was issued. The release of guidelines often fails to change professional behavior due to multiple barriers. Structured implementation may improve adherence to guidelines. This article describes the design of a study measuring the effect of an e-learning supported Train-the-Trainer program aiming at the training of the full staff of departments in the application of the guideline. We hypothesize that both professionals and departments will benefit from the program. Method In a multicenter cluster randomized controlled trial, 43 psychiatric departments spread over 10 regional mental health institutions throughout The Netherlands will be clustered in pairs with respect to the most prevalent diagnostic category of patients and average duration of treatment. Pair members are randomly allocated to either the experimental or the control condition. In the experimental condition, the full staff of departments, that is, all registered nurses, psychologists, physicians and psychiatrists (n = 532, 21 departments) will be trained in the application of the guideline, in a one-day small interactive group Train-the-Trainer program. The program is supported by a 60-minute e-learning module with video vignettes of suicidal patients and additional instruction. In the control condition (22 departments, 404 professionals), the guideline shall be disseminated in the traditional way: through manuals, books, conferences, internet, reviews and so on. The effectiveness of the program will be assessed at the level of both health care professionals and departments. Discussion We aim to demonstrate the effect of training of the full staff of departments with an e-learning supported Train-the-Trainer program in the application of a new clinical guideline. Strengths of the study are the natural setting, the training of full staff, the random allocation to the conditions, the large scale of the study and the willingness of both staff and management to participate in the study. Trial registration Dutch trial register: NTR3092

Machine learning (ML) is an increasingly popular approach/technique for analyzing "Big Data" and predicting risk behaviors and psychological problems. However, few published critiques of ML as an approach currently exist. We discuss some fundamental cautions and concerns with ML that are relevant when attempting to predict all clinical and forensic risk behaviors (risk to self, risk to others, risk from others) and mental health problems. We hope to provoke a healthy scientific debate to ensure that ML's potential is realized and to highlight issues and directions for future risk prediction, assessment, management, and prevention research. ML, by definition, does not require the model to be specified by the researcher. This is both its key strength and its key weakness. We argue that it is critical that the ML algorithm (the model or models) and the results are both presented and that ML needs to be become machine-assisted learning like other statistical techniques; otherwise, we run the risk of becoming slaves to our machines. Emerging evidence potentially challenges the superiority of ML over other approaches, and we argue that ML's complexity significantly limits its clinical utility. Based on the available evidence, we believe that researchers and clinicians should emphasize identifying, understanding, and explaining (formulating) individual clinical needs and risks and providing individualized management and treatment plans, rather than trying to predict or putting too much trust in predictions that will inevitably be wrong some of the time (and we do not know when). What is the public health significance of this article?: Machine learning is a statistical approach/technique that is increasingly being used in an attempt to improve the accuracy with which risky behaviors and mental health problems are predicted. This article discusses some key considerations for using machine learning and making it even more useful. [ABSTRACT FROM AUTHOR]

Objective: Combine test theory with technology to develop brief, reliable suicide risk measures in the emergency department.Methods: A computer adaptive test for suicide risk was built using the Beck Scale for Suicide Ideation and tested among the emergency department population. Data were analyzed from a sample of 1,350 patients in several Massachusetts emergency departments. The test was built as outlined by the National Institutes of Health Patient-Reported Outcomes Measurement Information System.Results: Of 1,350 patients, 74 (5%) scored above the cutoff of BSS > 2. Item 2, "Wish to die", was the most informative item. When using only Item 2, 20% (n = 15/74) of at-risk patients and 3% (n = 40/1,276) of not-at-risk patients were misclassified. Patients were classified after four items with computer adaptive testing trait estimates highly comparable to those of the full scale. The precision rule model did not reduce the scale.Conclusions: This study models the creation of a computer adaptive test for suicide ideation and marks the start of the development of computer adaptive tests as a novel suicide risk screening tool in the emergency department. Computer adaptive tests hold promise for revolutionizing behavioral health screening by addressing barriers including time and knowledge deficits. [ABSTRACT FROM AUTHOR]

Background: A reform of Dutch mental health care aimed to substitute care from specialized care to general practice. Since 1 January 2014, Dutch general practitioners (GPs) are no longer allowed to refer patients without a psychiatric disorder to mental health care. Patients with non-complex psychological problems should be treated within general practice.Objective: To explore the feasibility of the Dutch mental health policy.Methods: We conducted an observational case study in a primary health care centre in 2014. The health care centre was a convenience sample; the participating GPs reorganized mental health care in line with the upcoming policy, and invited the researchers to monitor their referrals. We assessed how many patients with mental health problems (n = 408) were allocated to policy-concordant treatment. Additionally, 137 patients (33%) completed a follow up assessment on mental health problems 3 months after baseline.Results: The majority of the patients were allocated to treatment in line with the policy. Almost half of the patients (42%) were treated in a setting that was exactly policy-concordant, while the other half (47%) was treated in a setting that was even less specialized than was allowed. In general, patients showed improvement after 3 months, regardless of (non) policy-concordant treatment. Attrition rate after 3 months was high, probably due to the practical study design.Conclusion: There is potential for substitution of mental health care. Since the studied health care centre was specialized in mental health care, further research should explore if similar results can be found in other general practices. [ABSTRACT FROM AUTHOR]

Background: Successfully implementing eMental health (eMH) interventions in routine mental health care constitutes a major challenge. Reliable instruments to assess implementation progress are essential. The Normalization MeAsure Development (NoMAD) study developed a brief self-report questionnaire that could be helpful in measuring implementation progress. Based on the Normalization Process Theory, this instrument focuses on 4 generative mechanisms involved in implementation processes: coherence, cognitive participation, collective action, and reflexive monitoring.Objective: The aim of this study was to translate the NoMAD questionnaire to Dutch and to confirm the factor structure in Dutch mental health care settings.Methods: Dutch mental health care professionals involved in eMH implementation were invited to complete the translated NoMAD questionnaire. Confirmatory factor analysis (CFA) was conducted to verify interpretability of scale scores for 3 models: (1) the theoretical 4-factor structure, (2) a unidimensional model, and (3) a hierarchical model. Potential improvements were explored, and correlated scale scores with 3 control questions were used to assess convergent validity.Results: A total of 262 professionals from mental health care settings in the Netherlands completed the questionnaire (female: 81.7%; mean age: 45 [SD=11]). The internal consistency of the 20-item questionnaire was acceptable (.62≤alpha≤.85). The theorized 4-factor model fitted the data slightly better in the CFA than the hierarchical model (Comparative Fit Index=0.90, Tucker Lewis Index=0.88, Root Mean Square Error of Approximation=0.10, Standardized Root Mean Square Residual=0.12, χ22=22.5, P≤.05). However, the difference is small and possibly not outweighing the practical relevance of a total score and subscale scores combined in one hierarchical model. One item was identified as weak (λCA.2=0.10). A moderate-to-strong convergent validity with 3 control questions was found for the Collective Participation scale (.47≤r≤.54, P≤.05).Conclusions: NoMAD's theoretical factor structure was confirmed in Dutch mental health settings to acceptable standards but with room for improvement. The hierarchical model might prove useful in increasing the practical utility of the NoMAD questionnaire by combining a total score with information on the 4 generative mechanisms. Future research should assess the predictive value and responsiveness over time and elucidate the conceptual interpretability of NoMAD in eMH implementation practices. [ABSTRACT FROM AUTHOR]

Background: Even though various types of suicidality are observed in clinical practice, suicidality is still considered a uniform concept. To distinguish different types of suicidality and consequently improve detection and management of suicidality, we developed a clinical differentiation model for suicidality. We believe that the model allows for a more targeted assessment of suicidal conditions and improves the use of evidence-based treatment strategies. The differentiation model is based on the experience with suicidality that we have encountered in clinical practice. This model distinguishes 4 subtypes of entrapment leading to suicidality. The earliest description of this model and a proposal for usability research has been previously presented in a book chapter., Objective: In this study, we present the most recent version of the 4-type differentiation model of suicidality and a protocol for a study into the usability of the proposed model., Methods: The 4-type differentiation model of suicidality distinguishes the following subtypes: (1) perceptual disintegration, (2) primary depressive cognition, (3) psychosocial turmoil, and (4) inadequate coping or communication. We plan to test the usability of the 4 subtypes in a pilot study of 25 cases, and subsequently, we will include 75 cases in a follow-up study. We looked at the case notes of 100 anonymized patients with suicidality who presented to mental health care emergency service in The Hague International Center. The summary and conclusions of the letters sent to the patients' general practitioners after suicide risk assessment will be independently rated by 3 psychiatrists and 3 nurse-scientists for absolute and dimensional scores. The Suicidality Differentiation version 2 (SUICIDI-II) instrument, developed for this study, is used for rating all the cases. Intraclass correlation coefficients for absolute and dimensional scores will be calculated to examine type agreement between raters to examine the usability of the model and the feasibility of the SUICIDI-II instrument., Results: We consider the model tentatively valid if the intraclass correlation coefficients are ≥0.70. Subsequently, if the model turns out to be valid, we plan to rate 75 other cases in a follow-up study, according to a similar or adjusted procedure. Study results are expected to be published by the end of 2023., Conclusions: The theoretical roots of the differentiation model stem from classic and contemporary theoretical models of suicidality and from our clinical practice experiences with suicidal behaviors. We believe that this model can be used to adjust the diagnosis, management, treatment, and research of suicidality, in addition to distinguishing different dynamics between practitioners and patients with suicidality and their families., International Registered Report Identifier (irrid): DERR1-10.2196/45438., (©Remco FP de Winter, Connie M Meijer, John H Enterman, Nienke Kool-Goudzwaard, Manuela Gemen, Anne T van den Bos, Danielle Steentjes, Gabrielle E van Son, Mirjam C Hazewinkel, Derek P de Beurs, Marieke H de Groot. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 11.08.2023.)

Objectives: To investigate care for patients with psychological or social problems provided by mental health nurses (MHNs), and by general practitioners (GPs) with and without MHNs., Design: An observational study with consultations recorded by GPs and MHNs., Setting: Data were routinely recorded in 161-338 Dutch general practices between 2010 and 2014., Participants: All patients registered at participating general practices were included: 624 477 patients in 2010 to 1 392 187 patients in 2014., Outcome Measures: We used logistic and Poisson multilevel regression models to test whether GPs recorded more patients with at least one consultation for psychological or social problems and to analyse the number of consultations over a 5-year time period. We examined the additional effect of an MHN in a practice, and tested which patient characteristics predicted transferral from GPs to MHNs., Results: Increasing numbers of patients with psychological or social problems visit general practices. Increasing numbers of GPs collaborate with an MHN. GPs working in practices with an MHN record as many consultations per patient as GPs without an MHN, but they record slightly more patients with psychological or social problems (OR=1.05; 95% CI 1.02 to 1.08). MHNs most often treat adult female patients with common psychological symptoms such as depressive feelings., Conclusions: MHNs do not seem to replace GP care, but mainly provide additional long consultations. Future research should study to what extent collaboration with an MHN prevents patients from needing specialised mental healthcare., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)