Bibliographic record
Abstract
INTRODUCTION: Primary care facilities in many parts of Quebec, Canada, are under pressure because of staff shortages, service instability, increasing requests from an aging population, and uncoordinated, fragmented delivery of care. Resource allocation in one facility is often made with an approximate knowledge of its impact on other facilities nearby. Unanticipated overflows may affect patients' health and staff morale. The main purpose of this study was to use consolidated administrative data in order to find the factors that better explain the choice of patients in Val Saint-Francois, a rural area of Quebec. METHODS: Administrative data relating to medical visits were linked to 6 primary care facilities over a period of 4 years. A classification tree algorithm generated users' profiles of facility choice, which was explored for frequency of use and related changes in preference, and for changes in levels of service. The factors used were: age, sex, postal c ode, and date of visit. RESULTS: Community was the major explanatory factor for patients' choice of facility, probably reflecting a tendency to use the closest facility. Older men and women tended to use appointment-based clinics more regularly than those who were younger. It was noted that younger men selected emergency rooms more often than young women, with the difference cancelling out as they age. The classification tree determined age thresholds for changing behaviours but also found dates when profiles changed within the same age-sex group. Later examination of service levels revealed that profile changes were subsequent to modifications in service operating hours. CONCLUSIONS: Evidence was found that predisposing factors (age and sex) with community enabling factors (distance) affected people's choice of healthcare facility. Changes in some patients' profiles corresponded to changes in service levels, proving that a modification of service hours in one facility affects demand in other facilities in a way that can be quantified. It is important to measure the effect of service changes on patients' choices for a more efficient allocation of resources.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".