An unexpected transition to virtual care: family medicine residents’ experience during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The global COVID-19 pandemic led to rapid changes in both medical care and medical education, particularly involving the rapid substitution of virtual solutions for traditional face-to-face appointments. There is a need for research into the effects and impacts of such changes. The objective of this article investigates the perspectives of Family Medicine Residents in one university program in order to understand the impact of this transition to virtual care and learning. METHODS: This is a qualitative focus group study. Four focus groups, stratified by site type (Rural = 1; Semi-Urban = 1; Urban = 2) were conducted, with a total of 25 participants. Participants were either first or second-year Residents in Family Medicine. Focus group recordings were analyzed thematically, based upon a five-level socio-ecological model (individual, family, organization, community, environment and policy context). RESULTS: Two main themes were identified: (1) Residents' experiences of Virtual Learning and Virtual Care, and (2) Living and Learning in Pandemic Times. In the first theme, Residents reported challenges both individually, in their family context, and in their training organizations. Of particular concern was the loss of hands-on experience with clinical skills such as conducting physical examinations. In the second theme, Residents reported disruption of self-care routines and family life. These Residents were unable to engage in the relationships outside of the workplace with their preceptors and peers which they had expected, and which play key roles in social support as well as in future decisions about practice location. CONCLUSIONS: While many patients appreciated virtual care, in the eyes of these Residents it is not the ideal modality for learning the practice of Family Medicine, and they awaited a return to normal times. Despite this, the pandemic has pointed out important ways in which residency training needs to adapt to an evolving world.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".