Modern work patterns of “classic” versus millennial family doctors and their effect on workforce planning for community-based primary care: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: There are ongoing accessibility challenges in primary care in British Columbia, Canada, with 17% of the population not having a regular source of care. Anecdotal evidence suggests that physicians are moving away from a community-based comprehensive practice model, which could contribute to shortages. Thus, we aimed to identify and describe how family physicians are currently organizing their primary care practices in a large health region in British Columbia and to examine differences between newer graduates and more established physicians. METHODS: Data for this cross-sectional study were drawn from an annual physician privileging survey. N = 1017 physicians were invited to participate. We categorized practice style into five distinct groupings and compared features across respondent groups, including personal and practice location characteristics, hospital and teaching work, payment and appointment characteristics, and scope of practice. We discuss the implications of styles of practice and associated characteristics on health workforce policy and planning. RESULTS: We received responses from 525 (51.6%) physicians. Of these, 355 (67.6%) reported doing at least some community-based primary care. However, only 112 (21.3%) provided this care full time. Most respondents supplemented community-based work with part-time hours in focused practice, hospitals, or inpatient facilities. We found diversity in the scope and style of practice across practice models. Compared to established physicians, new graduates (in practice less than 10 years) work more weekly hours (more patient care, and paperwork in particular). However, we found no difference between new and established physicians in the odds of providing any or full-time community-based primary care. CONCLUSIONS: Despite a lack of formalized structural reform in British Columbia's primary care system, most physicians are finding alternative ways to model their practice and shifting away from work at single-location, community-based clinics. This shift challenges assumptions that are relied on for workplace planning that is intended to ensure adequate access to longitudinal, community-based family medicine.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".