Practice-based small group learning: what are the motivations to become and continue as a facilitator? A qualitative study.
Bibliographic record
Abstract
INTRODUCTION: Practice-based small group learning (PBSGL) originated in Canada and has spread to Scotland. After a successful pilot in 2004, there has been rapid growth in the number of participants in Scotland, particularly among general practitioners (GPs). Growth of participant numbers has required the recruitment and retention of trained peer facilitators who help PBSGL groups to learn. It was not known what the perceptions and experiences of PBSGL facilitators were; in particular what had motivated them to become and continue as facilitators. METHOD: Two focus groups of PBSGL facilitators were held; their discussions were audio-recorded and transcribed with permission. Data generated were coded, and themes were constructed from these codes. RESULTS: Participants found facilitation work to be enjoyable and useful. They had positive past experiences of problem-based learning and of small group learning. Older facilitators had experiences gained through their involvement in GP registrar training. Some of the younger facilitators saw the programme as being a method to enhance and advance their careers. There were anxieties about recruiting new PBSGL groups from potential members relatively unknown to facilitators. Once groups were established, facilitators felt there was little need for further support. DISCUSSION: Participants were enthusiastic about PBSGL facilitation, suggesting that the programme will continue and may grow further. Their positive perceptions and experiences should reassure potential new facilitators.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".