Practice-Based Small Group Learning (PBSGL) in Scotland: the past, the present and the future
Bibliographic record
Abstract
Practice-based Small Group Learning (PBSGL) originated in Canada and transferred to Scotland in 2003 with a successful pilot involving 45 general practitioners (GPs). The Scottish programme has grown considerably since then and now has 3,400 members drawn from GPs, GP nurses, pharmacists and other professions. Members get together in small groups and discuss case presentations written by authors who have drawn on their own experiences with real patients. The group review a distillation of the current evidence base included in the module and propose changes to their own practice. Members make a commitment to change and log these changes in a shared document.In Scotland, 34% of groups are inter-professional, reflecting the dynamic changes to the primary health care team as it meets the health care needs of the Scottish population. Professional (and inter-professional) socialisation is a key feature of many PBSGL groups. Some groups have peer support as a central function to their meetings.The programme has recruited a small team of module writers and authors and most modules are now produced in Scotland by primary health care members. In addition, over 1,000 members have been trained up to be peer facilitators for their small group. The PBSGL programme in Scotland has ensured that continuing professional development of the primary health care team is available to teams across Scotland and that PBSGL groups can control the content and logistics of their own meetings.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".