Applying evidence in practice through small-group learning: a qualitative exploration of success
Bibliographic record
Abstract
Background A particular approach to continuing professional development for general practitioners originated in Canada. The Canadian approach uses a modification of problem-based learning that is based on evidence-based medicine with facilitated small groups. Evidence-based modules are developed for discussion in a small group, where the group exists over an extended period of time. An evaluation of a pilot of the ‘practice-based small group learning’ (PBSG) approach in Scotland demonstrated enhanced participant knowledge and skills in evidence-based practice and small-group working. However, it is not known why PBSG was successful. Understanding this will help inform any further research and development of the approach for general practitioners and other professional groups.Aim The aim of this study was to explore the perceptions and experiences of PBSG participants to gain an understanding of how PBSG learningachieves its success.Method A qualitative study of PBSG learning using one-to-one interviews.Results The small group format is an important factor in the success of the approach, along with the crucial role of the facilitator. Other factors include: the strong need among general practitioners to update their skills and compare their practice with that of peers; the inclusive nature of the small-group environment; the importance of creating a learning environment that is the right balance between being not too cosy but not too threatening; a recognition of the learning power of the group members instead of invited experts; the lack of trust among partners in practice and the lack of confidence of participants in their own skills as a facilitator. The findings highlight the importance of a learning environment conducive to learning and change, one that is based on honesty, openness and a willingness to acknowledge ignorance as a precursor to learning.
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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 | 0.057 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".