The Practice-based Small Group Learning Programme: Experiences of learners in multi-professional groups
Bibliographic record
Abstract
This study describes the experiences of General Practitioners (GPs) and Practice Nurses (PNs) as they came together and engaged in the Practice-based Small Group Learning (PBSGL) programme. Based on principles of adult and small group learning, PBSGL, which was developed in Canada, was used for the first time for the Continuing Professional Development (CPD) of multi-professional groups in the UK. The findings detail the main reasons GPs and PNs participated in PBSGL, the nature of interaction and development of cohesion in the groups, factors influencing contribution to discussions, the learning process, and outcomes for learners. Respect shown for different roles and perspectives enabled participants to be open about gaps in their knowledge and to ask questions. A mutual keenness to understand the perspectives of and learn from the other profession emerges as a key ingredient for learners to feel that their learning needs were met. The learning process in the groups came close to transformative learning--there were changes in perspectives, acquisition of new knowledge and increased self-esteem. The appropriateness of the PBSGL approach for the CPD of mixed groups of GPs and PNs is discussed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.010 |
| 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".