Aligning family medicine residency learning outcomes with societal needs: a nominal group study
Bibliographic record
Abstract
BACKGROUND: Current evidence suggests that family physicians, who have traditionally provided a wide range of services across various settings, are choosing to reduce their scope of practice. This can have negative effects on communities that rely on family physicians to meet their diverse primary care needs. The College of Family Physicians of Canada (CFPC) spearheaded a curriculum review and renewal of postgraduate residency training to support broad-scope family medicine and specifically address six areas of evolving societal need: (i) home and long-term care; (ii) addiction and mental health; (iii) Indigenous health; (iv) health equity and anti-racism; (v) virtual care and health informatics; and (vi) leadership, advocacy, and scholarship. This study answered the following research question: Which learning outcomes for the six areas of societal need identified by the CFPC should be prioritized in family medicine postgraduate education, according to local stakeholders? METHODS: As part of a broad investigation, we conducted a descriptive study using the nominal group technique. Participants—researchers, clinical educators, and residents affiliated with a Canadian university—first generated ideas for learning outcomes and rated each idea’s importance on a five-point Likert scale. Then, they selected the five ideas per area of societal need that they believed to be of highest priority. RESULTS : Fifty-nine participants took part in six sessions in the fall of 2023. They generated and prioritized 227 learning outcomes, emphasizing topics related to patient-centred care and to personal and professional skills in care delivery. They also highlighted the need for both culturally safe care and a better understanding of the history of inequity in health care for marginalized populations. CONCLUSIONS : This study generated and prioritized many learning outcomes relevant to the six areas identified by the CFPC. Combined with national-level work and international consultations, this work provides actionable learning outcomes for family medicine residency training programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".