Living and working in rural healthcare during the COVID-19 pandemic: a qualitative study of rural family physicians' lived experiences
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has been pervasive in its impact on all aspects of Canadian society. Along with its pervasiveness, the disease provided unprecedented complexity to the Canadian healthcare infrastructure, eliciting varying responses from the afflicted healthcare systems in Canada. However, insights into the various parameters and complexities endured by Canadian rural physicians and rural healthcare institutions during the pandemic have been scarce. OBJECTIVE: This paper explores the conditions and complexity of living and working of Rural Family Physicians (RFPs) in rural healthcare in Canada during the pandemic. METHODS: Community-based participatory research was utilized as a collaborative and partnership approach, equitably engaged community members in all aspects of research, ranging from designing the research question to analyzing data. Participants of this study include RFPs with at least one year of experience working in rural Canada. Data were collected through telephone interviews and analyzed according to the six-phase guide for the data's inductive thematic analysis. Data collection halted upon saturation. RESULTS: Five significant compiled categories reflect the lived experiences of Rural Family Physicians. 1- virtual care as a challenge or forward progress; 2- canceling in-person visits and interrupting the routine; 3- shortage of health care providers and supporting staff; 4-ongoing coping process with the pandemic guidelines; 5-COVID-19 combat fatigue. DISCUSSION: The inception of COVID-19 has significantly impacted rural physicians across several interconnected issues. This study illuminates the lesser-known effects of the COVID-19 pandemic, which heavily impacts rural healthcare.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".