Assessing Unperceived Learning Needs in Continuing Medical Education for Primary Care Physicians: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Assessing needs before developing continuing medical education/continuing professional development (CME/CPD) programs is a crucial step in the education process. A previous systematic literature review described a lack of objective evaluation for learning needs assessments in primary care physicians. This scoping review updates the literature on uses of objective evaluations to assess physicians' unperceived learning needs in CME/CPD. Identifying and understanding these approaches can inform the development of educational programs that are relevant to clinical practice and patient care. The study objectives were to (1) scope the literature since the last systematic review published in 1999; (2) conduct a comprehensive search for studies and reports that explore innovative tools and approaches to identify physicians' unperceived learning needs; (3) summarize, compare, and classify the identified approaches; and (4) map any gaps in the literature to identify future areas of research. METHODS: A scoping review was used to "map" the literature on current knowledge regarding approaches to unperceived needs assessment using conceptual frameworks for planning and assessing CME/CPD activities. RESULTS: Two prominent gaps were identified: (1) performance-based assessment strategies are highly recommended in nonresearch articles yet have low levels of implementation in published studies and (2) analysis of secondary data through patient input or environmental scanning is emphasized in grey literature implementation strategies more so than in peer-reviewed theoretical and research articles. DISCUSSION: Future evaluations should continue to incorporate multiple strategies and focus on making unperceived needs assessments actionable by describing strategies for resource management.
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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 | 0.056 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.028 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".