The role of distributed education in recruitment and retention of family physicians
Bibliographic record
Abstract
BACKGROUND: Distributed medical education (DME) programmes, in which training occurs in underserviced areas, have been established as a strategy to increase recruitment and retention of new physicians following graduation to these areas. Little is known about what makes physicians remain in the area in which they train. OBJECTIVES: To explore the factors that contributed to family physician's decisions to practice in an underserviced area following graduation from a DME programme. METHODS: Semistructured inperson interviews were conducted with 19 family physicians who graduated from a DME residency training programme. Programme records were reviewed to identify practice location of DME programme graduates. RESULTS: Of the 32 graduates to date from this DME programme, 66% (N=21) and all of the interview participants established their practices in this region after completing their residency training. Five key themes were identified from the interview analysis as impacting physicians' decisions to establish their practice in an underserviced area following graduation: familial ties to the region, practice opportunities, positive clerkship and residency experiences, established relationships with specialists and services in the area and lifestyle opportunities afforded by the location. CONCLUSIONS: This study suggests that DME programmes can be an effective strategy for equalising the distribution of family physicians and highlights the ways in which these programmes can facilitate recruitment and retention in underserviced areas, including being responsive to residents' personal preferences and objectives for learning and shaping their residency experiences to meet to these objectives.
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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 | 0.023 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".