Enhancing readiness for comprehensive care practice: a baseline survey for family medicine residency
Bibliographic record
Abstract
Introduction According to the Accreditation Standards for medical schools in North America the curriculum of a medical education program must include clinical experience in primary care ( pc ). Palliative and End-of-Life Care ( pe o lc ) is an essential component of pc and thus, it is the responsibility of medical schools and accreditation bodies to ensure adequate coverage. In Canada, primary care is represented by the specialty of Family Medicine ( fm ); therefore, understanding what learning experiences students have prior to fm residency is important for curriculum planning. Objective To highlight the findings in regards to pe o lc of a pilot survey completed by incoming fm residents about their experiences in medical school, and their future intentions to practice. Design Data were collected from residents in five fm programs across Canada who volunteered to participate in the pilot study in 2012 (n=317; response rate=69.8%); and seven programs in 2013 (n=449; response rate=88.9%). The survey consisted of multiple choice and Likert scale items. Data collection took place immediately upon entry to the fm residency program. Results 51% of residents in the 2012 cohort and 54.2% in the 2013 cohort reported no to minimal exposure to pe o lc , while only 2.8% in the 2012 cohort and 2.7% in the 2013 cohort reported a great deal of exposure. Regarding future practice intentions, 31.2% of residents in the 2012 cohort, and 23.6% in the 2013 cohort reported being either not at all likely or not likely to provide pe o l . Conclusions Participants’ self-reported exposure to different fm domains reflects important deficiencies in the scope of comprehensive care covered in medical schools. A big gap in exposure and intentions to practice pe o lc compared to other areas was identified. This baseline data may help curriculum planners consider the redesign of the undergraduate and postgraduate curricula to help medical trainees achieve their expected pe o lc competencies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".