Exploring rural doctors’ early experiences of coping with the emerging COVID‐19 pandemic
Bibliographic record
Abstract
PURPOSE: To understand how rural doctors (physicians) responded to the emerging COVID-19 pandemic and their strategies for coping. METHODS: Early in the pandemic doctors (physicians) who practise rural and remote medicine were invited to participate through existing rural doctors' networks. Thirteen semi-structured interviews were conducted with rural doctors from 11 countries. Interviews were transcribed verbatim and coded using NVivo. A thematic analysis was used to identify common ideas and narratives. FINDINGS: Participants' accounts described highly adaptable and resourceful responses to address the crisis. Rapid changes to organizational and clinical practices were implemented, at a time of uncertainty, anxiety, and fear, and with limited information and resources. Strong relationships and commitment to their colleagues and communities were integral to shaping and sustaining these doctors' responses. We identified five common themes underpinning rural doctors' shared experiences: (1) caring for patients in a context of uncertainty, fear, and anxiety; (2) practical solutions through improvising and being resourceful; (3) gaining community trust and cooperation; (4) adapting to unrelenting pressures; and (5) reaffirming commitments. These themes are discussed in relation to the Lazarus and Folkman stress and coping model. CONCLUSIONS: With limited resources and support, these rural doctors' practical responses to the COVID-19 crisis underscore strong problem-focused coping strategies and shared commitments to their communities, patients, and colleagues. They drew support from sharing experiences with peers (emotion-focused coping) and finding positive meanings in their experiences (meaning-based coping). The psychosocial impact on rural doctors working at the limits of their adaptive resources is an ongoing concern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".