Beyond the CLAIM: A comprehensive needs assessment strategy for creating an Advanced Medical Education Research Training Program (ARMED‐MedEd)
Bibliographic record
Abstract
BACKGROUND: The health professions education (HPE) landscape has shifted substantively with increasing professionalization of research and scholarship. Clinician educators seeking to become competitive in this domain often pursue fellowships and master's degrees in HPE, but there are few resources for the continuing professional development (CPD) of those who seek to continue developing their scholarly practice within HPE. Acknowledging the multiple players in this landscape, the authors sought to design a new "beyond beginners" HPE research program using a novel needs assessment planning process. METHODS: The authors developed and conducted a new three-phase, five-step process that sets forth a programmatic approach to conducting a needs assessment for a CPD course in HPE research. The five steps of the CLAIM method are: Competitive analysis, Literature review with thematic analysis, Ask stakeholders, Internal review by experts, and Mapping of a curriculum. These steps are organized into three phases (Discovery, Convergence, and Synthesis). RESULTS: Over a 12-month period, the authors completed a comprehensive needs assessment. The CLAIM process revealed that longitudinal digital connection, diverse and in depth exposure to HPE research methods, skills around scholarly publishing, and leadership and management of research would be beneficial to our design. CONCLUSIONS: The CLAIM method provided scaffolding to help the authors create a robust curriculum that adopts a scholarly approach for developing a HPE research course. This needs assessment methodology may be useful in other CPD contexts.
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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 | 0.129 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".