Assessing a research training programme for rural physicians
Bibliographic record
Abstract
INTRODUCTION: To assess the effect of a training programme called 6for6 (the programme) on research competency and productivity amongst rural physicians. The programme develops the research skills of six rural physicians over six weekends. Physicians learn about various research methods and writing techniques through blended learning components. METHODS: We conducted a quasi-experimental study, comparing research competency and productivity between intervention and non-equivalent control groups and over time through a repeated measures design. Generalized linear mixed model (GLMM), ANOVA, and Cochran Q tests were conducted. The intervention was provided to five groups of 6 rural physicians each between 2014 and 2019. Main outcome measures: self-assessed research competency (knowledge, attitudes and skills) and productivity (publications, grants and presentations of research-related work at conferences) were our primary and secondary outcomes, respectively. We measured the outcomes before, during and after the programme. Controls: Rural physicians who expressed interest in the programme and later enrolled. RESULTS: This study shows that, amongst its thirty participants, overall research competency was significantly different between intervention and control groups (65.7% ± 37.6% and 58.6% ± 14.4%, P < 0.05 for GLMM). The percentage of participants who were productive before, during and after the programme was 26.7%, 16.7% and 50.0%, respectively. Overall, productivity rates were significantly different between intervention and control groups (rate difference was 72.2/100 person-years, P < 0.05 for GLMM). CONCLUSION: This study suggests that the programme improves research competency and productivity for rural physicians. Rural physicians who wish to improve their research competency would benefit from participating in similar programmes.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".