Payment model impact on the resilience of rural communities
Bibliographic record
Abstract
OBJECTIVE: To explore rural physician perspectives on how remuneration impacted their experiences of contributing to community resilience during the COVID-19 pandemic. DESIGN: Exploratory, qualitative subanalysis. SETTING: Twenty-two rural communities in 4 Canadian provinces. PARTICIPANTS: Family physicians, other health care professionals, and patients in rural communities in British Columbia, Alberta, Saskatchewan, and Ontario. METHODS: Semistructured, virtual interviews conducted between November 2021 and February 2022 were included in the subanalysis. Interviews were audiorecorded, transcribed, coded, and analyzed thematically. MAIN FINDINGS: Participants expressed working under an alternative payment plan (APP) model facilitated greater engagement in their communities and said they were generally fairly compensated for nonclinical duties. Increased time allotted to each patient re-centred care priorities to meet the long-term needs of the community. Finally, APP physicians stated their systems of care supported their own wellness throughout the pandemic. CONCLUSION: Findings suggest physicians working in an APP model felt they had increased ability to engage with the community and contribute to its resilience. The flexibility of APPs may allow for more physician involvement in community sustainability that is not directly related to patient care.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".