An initiative to improve mental health practice in primary care in Caribbean countries
Bibliographic record
Abstract
Objectives: The aim of this initiative was to assess whether a novel training program - Understanding Stigma and Strengthening Cognitive Behavioral Interpersonal Skills - could improve primary health care providers' confidence in the quality of mental health care they provide in the Caribbean setting by using the Plan-Do-Study-Act rapid cycle for learning improvement. Methods: We conducted a prospective observational study of the impact of this training program. The training was refined during three cycles: first, the relevance of the program for practice improvement in the Caribbean was assessed. Second, pilot training of 15 local providers was conducted to adapt the program to the culture and context. Third, the course was launched in fall 2021 with 96 primary care providers. Pre- and post-program outcomes were assessed by surveys, including providers' confidence in the quality of the mental health care they provided, changes in stigma among the providers and their use of and comfort with the tools. This paper describes an evaluation of the results of cycle 3, the official launch. Results: A total of 81 participants completed the program. The program improved primary care providers' confidence in the quality of mental health care that they provided to people with lived experience of mental health disorders, and it reduced providers' stigmatization of people with mental health disorders. Conclusions: The program's quality improvement model achieved its goals in enhancing health care providers' confidence in the quality of the mental health care they provided in the Caribbean context; the program provides effective tools to support the work and it helped to empower and engage clients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".