Adolescent medicine training in postgraduate family medicine education: a scoping review
Bibliographic record
Abstract
Abstract Introduction Adolescents and young adults require age-appropriate healthcare services delivered by clinicians with expertise in adolescent medicine. However, resident family physicians report a low perceived self-efficacy and under-preparedness to deliver adolescent medical care. We conducted a scoping review to map the breadth and depth of the current evidence about adolescent medicine training for family medicine residents. Content We followed Arksey and O’Malley’s framework and searched seven electronic databases and key organizations’ webpages from inception to September 2020. Informed by the CanMEDS-FM, we analyzed the extracted data concerning basic document characteristics, competencies and medical topics using numerical and qualitative content analysis. Summary We included 41 peer-reviewed articles and six adolescent health competency frameworks (n=47). Most competencies taught in family medicine programs were organized under the roles of family medicine expert (75%), communicator (11.8%), and professional roles (7.9%). Health advocate and leader were rarely included (1.3%), and never scholar . Outlook The omission of multiple competency roles in family medicine resident education on adolescents is insufficient for family physicians to deliver optimal care to adolescents. The combined efforts of family medicine stakeholders to address adolescent medicine competency gaps may positively impact the perceived competence reported by family medicine residents.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".