Disentangling the effects of disability and age on health service utilisation
Bibliographic record
Abstract
PURPOSE: This article examines two competing hypotheses for the impact of disability and age on health service utilisation in Canada: the double jeopardy and age-as-leveller hypotheses. METHOD: The study uses a retrospective cohort design to examine the effect of age and disability on four aspects of health service utilisation: family doctor, medical specialist, hospital and homecare. The cohort was assembled from the longitudinal component of the National Population Health Survey. The effective sample size for this analysis was 1629. RESULTS: This study showed that disability is a stronger predictor of doctor and hospital utilisation than age. No significant relationship was found between age and specialist use, and there were only small to moderate increases in the use of family doctors and hospitals with each 5-year increment of age over 65. There is a strong association between the use of home care and both age and disability. Results support the age-as-leveller hypothesis, in that negative interaction effects were found between age and disability for use of both family physicians and medical specialists. In other words, age and disability together have an effect that is less than would be expected, given the main effects of each. CONCLUSION: The results of this study support the importance of disability as an indicator of health service utilisation. Rehabilitation practitioners are encouraged to continue to sensitise other members of the health care team to the importance of disability as a way of understanding health and health service use.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".