Exploring stakeholders’ views of medical education research priorities: a national survey
Bibliographic record
Abstract
CONTEXT: Setting research priorities is important when exploring complex issues with limited resources. Only two countries (Canada and New Zealand) have previously conducted priority-setting exercises for medical education research (MER). This study aimed to identify the views of multiple stakeholders on MER priorities in Scotland. METHODS: This study utilised a two-stage design to explore the views of stakeholders across the medical education continuum using online questionnaires. In Stage 1, key informants outlined their top three MER priorities and justified their choices. In Stage 2, participants rated 21 topics generated in Stage 1 according to importance and identified or justified their top priorities. A combination of qualitative (i.e. framework analysis) and quantitative (e.g. exploratory factor analysis) data analyses were employed. RESULTS: Views were gathered from over 1300 stakeholders. A total of 21 subthemes (or priority areas) identified in Stage 1 were explored further in Stage 2. The 21 items loaded onto five factors: the culture of learning together in the workplace; enhancing and valuing the role of educators; curriculum integration and innovation; bridging the gap between assessment and feedback, and building a resilient workforce. Within Stage 2, the top priority subthemes were: balancing conflicts between service and training; providing useful feedback; promoting resiliency and well-being; creating an effective workplace learning culture; selecting and recruiting doctors to reflect need, and ensuring that curricula prepare trainees for practice. Participant characteristics were related to the perceived importance of the factors. Finally, five themes explaining why participants prioritised items were identified: patient safety; quality of care; investing for the future; policy and political agendas, and evidence-based education. CONCLUSIONS: This study indicates that, across the spectrum of stakeholders and geography, certain MER priorities are consistently identified. These priority areas are in harmony with a range of current drivers in UK medical education. They provide a platform of evidence on which to base decisions about MER programmes in Scotland and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".