What Is the Impact on Rural Area Residents When the Local Physician Leaves?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Scarce evidence exists in the medical literature describing the attitudes of rural community residents about the impact of losing their local physician. This pilot study explores aspects of access to care, both within and outside of primary care settings, that result from loss of a rural family physician. METHODS: We selected study participants through convenience and snowball sampling, and we conducted in-person interviews of up to 60 minutes. We audio recorded and transcribed the interviews (May to August, 2018), then analyzed transcripts using immersion crystallization and managed within Atlas.ti 7.0 software (Berlin, Germany). RESULTS: We interviewed 18 participants, some of whom interviewed as pairs. Our analysis revealed three significant themes: rurally-specific access to care concerns, relationships valued for being both community and care based, and loss felt specific to the integrated community leadership roles occupied by family physicians. In addition, participants identified social challenges they associated with losing their "country doctor," such as withering community cohesion. CONCLUSIONS: Our findings suggest that rural physicians offer tremendous value to their communities, both inside and beyond their clinic walls. Issues of social cohesion and local health leadership affected by physician loss should be addressed by policy makers and educators charged with designing patient-centered solutions to improve health outcomes in rural communities. Current health and medical education reforms would benefit from greater focused attention on these issues.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".