A tailored educational intervention improves doctor's performance in managing depression: a randomized controlled trial
Bibliographic record
Abstract
RATIONAL AND OBJECTIVES: To assess the effects of a tailored and activating educational intervention, based on a three-stage modified Prochaska model of readiness-to-change, on the performance of general physicians in primary care (GPs) regarding management of depressive disorders. METHODS: Parallel group, randomized control trial. Primary hypothesis was that performance would improve by 20 percentage units in the intervention arm. The setting was primary care in southern Tehran. The participants were 192 GPs stratified on stage of readiness-to-change, sex, age and work experience. The intervention was a 2-day interactive workshop for a small group of GPs' at a higher stage of readiness-to-change ('intention') and a 2-day interactive large group meeting for those with lower propensity to change ('attitude') at the pre-assessment. GPs in the control arm participated in a standard educational programme on the same topic. The main outcome measures were validated tools to assess GPs' performance by unannounced standardized patients, regarding diagnosis and treatment of depressive disorders. The assessments were made 2 months before and 2 months after the intervention. RESULTS: GPs in the intervention arm significantly improved their overall mean scores for performance regarding both diagnosis, with an intervention effect of 14 percentage units (P = 0.007), and treatment and referral, with an intervention effect of 20 percentage units (P < 0.0001). The largest improvement after the intervention appeared in the small group: 30 percentage units for diagnosis (P = 0.027) and 29 percentage units for treatment and referral (P < 0.0001). CONCLUSIONS: Activating learning methods, tailored according to the participants' readiness to change, improved clinical performance of GPs in continuing medical education and can be recommended for continuing professional development.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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) | 0.009 | 0.001 |
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".