Family Physicians’ Roles in Long-Term Care Homes and Other Congregate Residential Care Settings during the COVID-19 Pandemic: A Qualitative Study
Bibliographic record
Abstract
Context: The COVID-19 pandemic disproportionally affected long-term care (LTC) homes and other community-based congregate residential care settings. Although family physicians (FPs) play important roles in the care of residents in LTC homes, provincial pandemic plans make few references to their specific roles in LTC. Objective: To examine the experiences of FPs providing care in LTC homes and other congregate care settings in Canada during the first year of the COVID-19 pandemic (2020–2021). Methods: As part of a multiple case study, we conducted semi-structured qualitative interviews with FPs across four Canadian regions. Interviews were transcribed, and a thematic analysis approach was employed. Findings: Twenty-one of the 68 FPs interviewed discussed providing care in congregate residential settings, including LTC. We identified three major themes: 1) the roles of FPs in community-based congregate residential care settings during a pandemic, 2) modification of the delivery of routine care, and 3) special workforce considerations in pandemic response for community-based congregate residential care settings. Limitations: We interviewed FPs in four Canadian jurisdictions between October 2020 and June 2021; findings may not be generalisable to later pandemic stages or to other provinces. Our recruitment strategy did not specifically target FPs who worked in different types of congregate residential care facilities; further research is needed to examine these settings in greater depth. Implications: FPs have a unique understanding of the populations they serve and are well suited to plan and implement community-adaptive procedures. Future pandemic plans should implement LTC-related FP roles during the pre-pandemic stage of a pandemic response.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".