Spatial Analysis of Healthcare Offer and Request for Older People Aged 65 Years and Over in Quebec
Bibliographic record
Abstract
Abstract With years, the health-needs of an individual become numerous and more complex, resulting in the requirement of an even more appropriate offer of health services. However, it is known that different factors make the services and the request of the population to health care unequally, especially the interregional variations. These are represented by a gap between the offer of healthcare and the need of the populations. The aim of this study was 1) to map the relationship between the location of the healthcare services in Quebec and population aged 65 years and over, and 2) to identify the characteristics related to the geographic variations in access to healthcare. We used data from “statcan.gc.ca”, “donneesquebec.ca” and “msss.gouv.qc.ca” regarding the facilities, their capacity, their services, and the populations’ characteristics. Analyses were performed on QGIS and R software. As expected, our results showed that there is a gap between the healthcare needs and the services: older people need a large amount of diverse services which are not always provided by secluded areas. Moreover, it also appeared that the deprivation index is related to the offer of health care. As this project takes part in a global project studying the health care trajectories of older people in Quebec using their administrative health databases, those findings will help better understand the impact of the geographic factors for the interregional variations of 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".