The support of early-career researchers in health professions education—an expert position statement
Bibliographic record
Abstract
Introduction: The development of health professions education (HPE) as an academic discipline requires well-qualified educational researchers, equipped with the competence to advance the field. There is, therefore, a need to establish and support pathways in which early-career researchers (ECRs) can develop the necessary competence to pursue a career in this field. Approach: A group of 19 international experts in HPE from various professions, conducted a 2.5-day Scoping Workshop in Hannover, Germany, in November 2024. The main output of the workshop is a joint position statement on the support of ECRs in HPE, using appreciative inquiry and collaborative writing. Position: The Scoping Workshop led to a dynamic and productive exchange of ideas and experiences resulting in a common vision and five positions: (1) identify, establish, and recognize distinct career paths, (2) develop and implement a robust funding strategy, (3) create a nurturing and diverse intellectual culture, (4) connect research to practice and address real-world problems, (5) invest in leadership, advocacy, and coaching. There was strong agreement that these areas were not well developed and required urgent attention. Outlook: There is a need to foster interprofessional and interdisciplinary collaboration and provision of sustainable support structures so that ECRs can advance HPE. Only when these areas are addressed can these educational researchers contribute to the development of effective learning which prepares the healthcare workforce to meet today's challenges. Researchers, educators, decision-makers and stakeholders in academia, education, and health and social care contexts share a responsibility for shaping the way forward.
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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 | 0.107 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.027 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".