Exploration of Rural Family Physicians’ Lived-Experiences of Coping with the COVID-19 Pandemic
Bibliographic record
Abstract
Context: The pervasiveness of the impact by COVID-19 has been felt across all aspects of Canadian society. In attempts to circumvent rises in unprecedented complexity, the pandemic has prompted varying responses from the afflicted healthcare systems in Canada. However, there is a scarcity of insights into the various parameters and complexities endured by Canadian rural physicians and rural healthcare institutions. Objective: This paper aims to explore the endured intricacies and difficulties by rural healthcare institutions and Canadian rural physicians during the pandemic. Study Design: The research was predicated on community-based participatory methodology which facilitated engagement among community members in all aspects of research; ranging from formulating the research question to analyzing data. Data are collected through in-depth telephone interviews; data collected was stopped upon reaching saturation. Setting: Rural and Remote communities in Canada. Population of study: Rural family physicians with at least one year of clinical practice experience. Recruitment: Rural family physicians are recruited via recruitment emails forwarded through the Society of Rural Physicians of Canada list server. The sampling methods used were purposive (e.g., years of practice) and snowball sampling. Analysis: All interviews are transcribed verbatim and thematically analyzed. Results: This study illustrates the findings of five major categories underpinning Rural Family Physicians’ lived-experiences: 1- Shifting to virtual care: double edge sword; 2- Improving accessibility or damaging care; 3-Healthcare personnel staffing shortage; 4- Coping with the pandemic guidelines; and 5-Work fatigue and pandemic fatigue. Conclusion: The COVID-19 pandemic has induced significant degrees of complexities on the experiences and livelihood of rural physicians. This study illuminates the lesser-known effects of the COVID-19 pandemic which is heavily impacting the infrastructure of 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".