Evaluation of a Training Program Prototype to Promote the Adoption of Clinical Mental Health Assessment Best Practices Among Primary Care Nurses: A Research Protocol
Bibliographic record
Abstract
Introduction: Nurses play an essential role in the detection and assessment of mental health issues. However, adopting best assessment practices in mental health remains challenging for primary care nurses (PCN). Objectives: This article presents a study protocol aimed at evaluating the acceptability and the perceived effects of a training program prototype to enhance the adoption of best practices in mental health assessment by PCNs. Additionally, the study explores the feasibility of involving PCNs clinical supervisors in the training process. Methods: The 6-week training program was developed using a living lab approach, combining individual and group activities, focusing on reflective practice and clinical support. A mixed-methods approach combining quantitative and qualitative designs will be used, with data collected from 10 to 20 PCNs and 2 to 4 clinical supervisors. Five questionnaires will assess the acceptability of the program (following each activity), while perceived effects will be explored through 3 questionnaires at 3 time points: pre-, immediately post-, and 4 to 6 months post-program. Qualitative interviews will also be conducted with PCNs and PCN clinical supervisors (immediately post- and 4 to 6 months post-program) to explore the acceptability, perceived effects of the program and the feasibility of involving PCN clinical supervisors in the program. Discussion and Research Spin-offs: By evaluating the acceptability and perceived effects of this innovative continuing education program for mental health assessment, this project could provide valuable insights for adapting and testing the program in other settings and with a broader population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.102 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".