General practitioners’ preferences for future continuous professional development: evidence from a Danish discrete choice experiment
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Danish general practitioners (GPs) follow a voluntary continuous professional development (CPD) programme based on accredited activities. Inspired by a current interest in CPD, this study investigates GPs' preferences for future CPD programmes. METHODS: The preferences were tested in a Discrete Choice Experiment (DCE) sent to 1079 randomly chosen GPs. The GPs were asked to choose between hypothetical CPD programmes, based on educational questions generated from discussions with educational stakeholders. RESULTS: The response rate was 686/1079 (63%). GPs had the following preferences for a future CPD programme: 1) option to exchange experience with colleagues, 2) focus on implementation of new knowledge into practice, 3) ensure 10 days of CPD activities per year, 4) to have CPD programmes where 50% are planned by a central organisation and 50% are planned by the individual GP, 5) to have teachers with a profound insight and knowledge about general practice. There was neither an overall request for appraisal, nor for more CPD activities based on interactive learning strategies. There was, however, variability in GPs' preferences regarding some of the elements. CONCLUSION: A prioritised list of Danish GPs' preferences for future CPD has been identified. However, variation in preferences suggests there should be room for individual variation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.074 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".