Practice based small group learning during a pandemic: an evaluation from Defence Primary Healthcare
Bibliographic record
Abstract
The educational benefits of Practice-Based Small Group Learning (PBSGL) are well known. The Ministry of Defence in the United Kingdom employs a salaried healthcare workforce across the globe with staff frequently moving. Given the success of PBSGL in Canada and Scotland, PBSGL was introduced as a large pilot to assess it as a continuous professional development (CPD) resource. A survey gathering quantitative and qualitative was distributed to the pilot population after using PBSGL for 12 months. This showed the favoured types of CPD were PBSGL and taught CPD update courses. Themes identified from free-text comments were: developing professional educational networks during Covid; evolving themes of CPD; applying learning to practice; practical aspects of delivering CPD to Defence promoting a positive learning environment; human interaction is therapeutic. These were similar to educational and non-educational benefits found in previous evaluations, but with the added benefit of providing a professional educational network during the COVID pandemic. Benefits were preserved when the sessions were run remotely using video-conferencing, although some of the human interaction was lost. As CPD, it was highly valued. For Defence, who need to consider the CPD requirements of their workforce, provision of PBSGL alongside taught CPD updates may satisfy the learning needs of the majority of the workforce.
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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 | 0.028 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".