Using a linked data set to determine the factors associated with utilization and costs of family physician services in Ontario: effects of self-reported chronic conditions.
Bibliographic record
Abstract
Evidence-based health care planning for persons with chronic conditions is difficult. Routinely collected data are not specific enough to obtain prevalence estimates for chronic conditions and accompanying health determinants, whereas available survey data do not provide accurate utilization and/or cost information. The purpose of this study was to determine the association of self- reported demographic factors (age, sex), access (having a regular doctor), socio-economic factors (education/income) and need (comorbidity) with actual family physician costs for persons with arthritis/rheumatism, asthma, back pain, high blood pressure and migraines. Data from consenting Ontario respondents to the 1994 Canadian National Population Health Survey were linked with provincial physician billing claims. More than half of Ontario adults aged 25 and over reported a chronic condition; 24% reported two or more. Age, sex, access, socio-economic status and need were independently associated with family practice utilization and costs, and the magnitude of the effects varied by condition. Linked survey/administrative data can provide valuable information to assist in evidence-based health care planning.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".