A Guide for Planning and Implementing Successful Mental Health Educational Programs
Bibliographic record
Abstract
INTRODUCTION: Considering the global burden of mental disorders, there is a worldwide need to improve the quality of mental health care. In order to address this issue, a change in how health care professionals are trained may be essential. However, the majority of the few reports published on this field's training programs do not discuss the characteristics associated with the success or failure of these strategies. The purpose of this review was to systematically examine the literature about mental health training programs designed for health care professionals in order to identify the relevant factors associated with their effective implementation. METHODS: The MEDLINE/PubMed, SciELO, and Virtual Health Library databases were used to search for articles published before February 2017 and reviewed by two double-blind reviewers. RESULTS: We found 77 original papers about mental health educational programs. Many of these studies were conducted in the USA (39%), addressed depression as the main subject (34%), and applied a quasi-experimental design (52%). Effective interventions were associated with the following characteristics: the use of learner-centered and interactive methodological approaches; a curriculum based on challenges in the trainees' daily routines; the involvement of experts in the program's development; the enrollment of experienced participants; interdisciplinary group work; flexible timing; the use of e-learning resources; and optimizing the implementation of knowledge into the participants' routine work practices. IMPLICATIONS FOR PRACTICE: These results will be helpful for planning and improving the quality of future educational programs in mental health.
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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 | 0.027 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.034 |
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".