Just regionalisation: rehabilitating care for people with disabilities and chronic illnesses
Bibliographic record
Abstract
BACKGROUND: Regionalised models of health care delivery have important implications for people with disabilities and chronic illnesses yet the ethical issues surrounding disability and regionalisation have not yet been explored. Although there is ethics-related research into disability and chronic illness, studies of regionalisation experiences, and research directed at improving health systems for these patient populations, to our knowledge these streams of research have not been brought together. Using the Canadian province of Ontario as a case study, we address this gap by examining the ethics of regionalisation and the implications for people with disabilities and chronic illnesses. The critical success factors we provide have broad applicability for guiding and/or evaluating new and existing regionalised health care strategies. DISCUSSION: Ontario is in the process of implementing fourteen Local Health Integration Networks (LHINs). The implementation of the LHINs provides a rare opportunity to address systematically the unmet diverse care needs of people with disabilities and chronic illnesses. The core of this paper provides a series of composite case vignettes illustrating integration opportunities relevant to these populations, namely: (i) rehabilitation and services for people with disabilities; (ii) chronic illness and cancer care; (iii) senior's health; (iv) community support services; (v) children's health; (vi) health promotion; and (vii) mental health and addiction services. For each vignette, we interpret the governing principles developed by the LHINs - equitable access based on patient need, preserving patient choice, responsiveness to local population health needs, shared accountability and patient-centred care - and describe how they apply. We then offer critical success factors to guide the LHINs in upholding these principles in response to the needs of people with disabilities and chronic illnesses. SUMMARY: This paper aims to bridge an important gap in the literature by examining the ethics of a new regionalisation strategy with a focus on the implications for people with disabilities and chronic illnesses across multiple sites of care. While Ontario is used as a case study to contextualize our discussion, the issues we identify, the ethical principles we apply, and the critical success factors we provide have broader applicability for guiding and evaluating the development of - or revisions to - a regionalised health care strategy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".