“It was horrible for that community, but not for the way we had imagined”: A qualitative study of family physicians’ experiences of caring for communities experiencing marginalisation during COVID-19
Bibliographic record
Abstract
The COVID-19 response required family physicians (FPs) to adapt their practice to minimise transmission risks. Policy guidance to facilitate enacting public health measures has been generic and difficult to apply, particularly for FPs working with communities that experience marginalisation. Our objective was to explore the experiences of FPs serving communities experiencing marginalisation during COVID-19, and the impact the pandemic and pandemic response have had on physicians' ability to provide care. We conducted semi-structured qualitative interviews with FPs from four Canadian regions, October 2020 through June 2021. We employed maximum variation sampling and continued recruitment until we reached saturation. Interviews explored participants' roles/experiences during the pandemic, and the facilitators and barriers they encountered in continuing to support communities experiencing marginalisation throughout. We used a thematic approach to analyse the data. FPs working with communities experiencing marginalisation expressed the need to continue providing in-person care throughout the pandemic, often requiring them to devise innovative adaptations to their clinical settings and practice. Physicians noted the health implications for their patients, particularly where services were limited or deferred, and that pandemic response policies frequently ignored the unique needs of their patient populations. Pandemic-related precautionary measures that sought to minimise viral transmission and prevent overwhelming acute care settings may have undermined pre-existing services and superseded the ongoing harms that are disproportionately experienced by communities experiencing marginalisation. FPs are well placed to support the development of pandemic response plans that appreciate competing risks amongst their communities and must be included in pandemic planning in the future.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.026 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".