Development and Evaluation of a Faculty-Based Accredited Continuing Professional Development Route for Teaching and Learning
Bibliographic record
Abstract
This article characterizes and evaluates the development of an accredited, in-house, faculty-based teaching recognition scheme aimed at supporting clinicians and academics to achieve Advance HE Fellowship recognition. The scheme takes 6 to 24 months to complete and forms part of an institution-wide scheme. The evaluation covered 44 months, collecting data on participation rates across the school and 21 semi-structured interviews across 16 staff participants. We describe the outcomes measured alongside key perceived benefits and challenges to support the implementation of similar schemes elsewhere. Across 130 academic staff, there was 61% engagement. In interviews, 11 participants characterized benefits in terms of changes to their teaching, such as adopting new strategies for differing class sizes, and highlighted the benefit of accessible and context-specific development opportunities designed specifically for STEMM (science, technology, engineering, mathematics, and medicine) practitioners and clinicians. Motivations for participating were mainly intrinsic (69%), with international professional recognition also featured (61%, n = 10). Of the 23 participants who withdrew, the largest subgroup (39%) withdrew because they had left the institution, and 35% withdrew because of a lack of time, which encompassed a range of issues. We outline recommendations for implementing similar schemes including protected time, accessible development opportunities, and support for mentors.
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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 | 0.070 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".