Choosing family medicine residency programs
Bibliographic record
Abstract
Objective To describe key determinants for residents’ selection of a new community-based, interprofessional site for their family medicine training, and to evaluate residents’ satisfaction with their programs. Design Combined qualitative and quantitative methods using in-depth interviews and a survey. Setting McMaster University, including the new site of the Centre for Family Medicine in Kitchener-Waterloo, Ont, and a long-established site in Hamilton, Ont. Participants Eleven first-year and second-year family medicine residents from the Kitchener-Waterloo site participated in in-depth interviews. Forty-four first-year and second-year family medicine residents completed the survey, 22 in Kitchener-Waterloo and 22 in Hamilton. Methods Kitchener-Waterloo residents participated in in-depth interviews during their residency programs in 2008 to 2009 using a semistructured format to explore their choice of site and the effect of an interprofessional environment on their education. Common themes were established using qualitative analysis techniques; based on these themes, a survey was developed and distributed to residents from both sites to further explore factors influencing site selection, satisfaction, and effects of interprofessional education. Main findings Residents identified several reasons for selecting a new community-based, interprofessional family medicine residency program. Reasons included preference for the location and opportunities to learn in an interprofessional teaching environment. A less hierarchical structure and greater opportunities for one-on-one teaching also influenced their choices. Perception of poor communication from the well established site was identified as a challenge. Residents at both sites indicated similarly high levels of program satisfaction. Conclusion Residents selected the new community-based family medicine site for reasons of geographic location and the potential for clinical learning experiences and interprofessional education. High program satisfaction was achieved at both the new and well established sites. Family medicine residency programs developing community-based networks might consider and encourage the positive influence of interprofessional care and education. Good communication between distributed sites remains a challenge.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".