Refining a Location Analysis Model Using a Mixed Methods Approach: Community Readiness as a Key Factor in Siting Rural Palliative Care Services
Why this work is in the frame
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Bibliographic record
Abstract
Drawing on recent debates pointing to the value of mixed methods research in human geography, the authors revisit a quantitative location analysis model previously created to site palliative care services in rural British Columbia, Canada. The original quantitative model posited that population (i.e., number of residents in the community), isolation (i.e., travel time to existing specialized palliative care), and vulnerability (i.e., number of residents older than 65 years in the community) are three factors that must be accounted for when siting palliative care services in rural areas. Using qualitative interview data, the authors refine this model to include a newly identified factor: community readiness. They conclude with a discussion of the benefits of adopting a mixed methods approach to location analysis model development.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it