Enhanced train-the-trainer program for registered nurses and social workers to apply the founding principles of primary care in their practice: a pre-post study
Bibliographic record
Abstract
BACKGROUND: A train-the-trainer approach can effectively support the integration of new practice standards for health and social services professionals. This study describes the effects of an enhanced train-the-trainer program to support registered nurses and social workers working in primary care clinics in their understanding of the fundamental principles of primary care. METHODS: We implemented an enhanced train-the-trainer program for registered nurses and social workers in six primary care clinics. We conducted a pre-post study using quantitative and qualitative data to assess trainers' and trainees' intention, commitment, and confidence in applying acquired knowledge. RESULTS: We trained 11 trainers and 33 trainees. All the trainers and trainees were satisfied with the program. Trainers were less confident in their abilities as trainers following the training, especially regarding tailored coaching (p = 0.03). Trainees' commitment to becoming familiar with the functioning of their clinic (p = 0.05) and becoming part of the team increased significantly (p = 0.01); however, their intention to use their knowledge decreased (p = 0.02). Trainers and trainees identified organizational and professional barriers that may explain the observed decrease. CONCLUSION: An enhanced train-the-trainer program positively impacted registered nurses' and social workers' assimilation of the fundamental principles of primary care. Further research is needed to understand the long-term effects of train-the-trainer programs on primary care trainees and how these effects translate into patient care.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".