Addressing the health needs of frail elderly people: Ontario’s experience with an integrated health information system
Bibliographic record
Abstract
Frail elderly people comprise a subpopulation that poses numerous important challenges for the health care system. In Canada, like most developed nations, per capita health care expenditures rise disproportionately with age [1]. Although there is widespread concern in the popular media that population ageing will have catastrophic financial consequences, it is generally felt that population ageing has played a minor role in rising health expenditures in Canada [2], and it will be possible to cope with the costs of caring for future cohorts of the elderly if we manage the health care system appropriately. Home care has become the fastest growing segment of the Canadian health care system, and a national commission on the Future of Health Care in Canada [3] described home care as ‘the next essential service’. Nonetheless, hospitals continue to be the leading source of health expenditure in Canada [4], and the rates of acute hospitalisation of Canadians aged 85 years and over are almost six times higher than those under 65 years [5]. The study by Jónsson and colleagues in this issue raises the troubling question of whether current care practices in acute hospitals provide an adequate response to the complex care needs of frail elderly people [6]. Indeed, for most countries, the same question could be posed for the health care system as a whole.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".