Effects on Knowledge and Attitudes of Using Stages of Change to Train General Practitioners on Management of Depression: A Randomized Controlled Study
Bibliographic record
Abstract
OBJECTIVE: To assess the impact on knowledge and attitudes of a tailored educational intervention on depression using a modified version of the Prochaska stages of change model, compared with standard continuing medical education, for general practitioners (GPs) in primary care in Iran. METHOD: Using a randomized controlled trial, a total of 192 GPs were evenly randomized to intervention or control arm. The topic for the educational intervention was depressive disorders. The participants were divided in to small and large groups, depending on their initial stage of change. The GPs' knowledge and skills regarding management of depressive disorders were assessed through a questionnaire with 7 multiple choice questions, 11 Likert statements, 3 case vignettes, and 1 essay question. Attitudes toward management of depressive disorders were also assessed. Both questionnaires were validated. RESULTS: There was a significant improvement in knowledge mean scores regarding multiple choice and Likert questions (intervention effect 6%; P = 0.002), as well as for the case vignettes and essay question (intervention effect 12%; P = 0.011) in the intervention arm, in comparison with the control arm. There were significant changes in mean attitude scores in both study arms, but no difference between them. CONCLUSIONS: A theoretical model of medical learning and behavioural change can be used to devise educational formats that suit different stages of learning. Such tailored educational formats can improve GPs' knowledge and skills regarding management of depressive disorders.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".