The Impact of COVID-19 on the Training and Practice Choices of Early Career Family Physicians
Bibliographic record
Abstract
Context: COVID-19 exacerbated the shortage of family physicians providing comprehensive care in Ontario. For family physicians in their first years of practice, they were faced with either receiving their family medicine training during the pandemic or, equally challenging, beginning their practice during COVID-19. Objective: To explore the impact of COVID-19 on the training and practice of early career family physicians (FPs), and the influence on their decision-making process to practice comprehensive care. Study Design and Analysis: Grounded theory study using in-depth interviews via Zoom, with individual and team analysis. Setting: FP practices in Ontario, Canada. Population Studied: 38 family physicians practicing in Ontario, who completed their residency training within the last 5 years. Results: Family Medicine (FM) residents experienced varying levels of COVID19-related disruptions, including an abrupt change to virtual care and fewer in-person community and clinic opportunities during their training. The impact of COVID-19 on participants included feeling isolated from other residents and staff and having less exposure to in-person procedures (e.g. minor procedures, OB) which made them less confident to perform these skills on graduation. Conversely, some described an increased skillset in acute medicine through redeployment or additional hospital-based rotations. Concurrently, new graduates in the COVID-era experienced challenges in their workforce entry, often during locums where there was reliance on virtual care, less on-site support and adapting to a disrupted system. They were simultaneously exposed to focussed FM opportunities that were part of a larger call to action such as vaccine clinics and assessment centres which they noted to be relatively highly remunerated, lower stress, and often a positive environment in terms of appreciative patients and socialization with colleagues. Conclusions: Findings reveal the impact of COVID-19 on the training and early career experiences of new graduates at a critical juncture in professional identity formation. Disruptions in the health system presented challenges to comprehensive FM care and offered attractive focussed practice choices. The findings have implications for educators and health workforce planning as the impact of COVID-19 on early career physicians needs further exploration and remedy to ensure comprehensive FM remains a viable choice going forward.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".