The Coordination of Aging in Place in Ontario: How Health System Design Impedes the Ability to Remain Living at Home
Bibliographic record
Abstract
This is a study of the experiences of older adults of advanced age living with complex health conditions, or frailty, and their caregivers as they carry out their everyday activities and work to remain living at home. Methods used combine an Institutional Ethnography (IE) inspired approach, an IE informed adaptation of the Listening Guide, a voice-centred relational approach, and qualitative interviewing with older adults and their caregivers (n=11). This investigation specifically examines how it happens that older people who intended to live out their lives at home may be unable to do so because of coordinating texts, including procedures, processes, policy, programs, legislation and regulation, activated by various actors, and implicated in an Ontario health system that is not designed for a major cohort of its population. Through qualitative data collection, empirical tracing, an extensive examination of aging in place literature, and a review of more than 160 coordinating texts currently activated in Ontario, this study models analytic techniques that can enable the inclusion of accounts from older adults in system design. Findings suggest the need for new skills for today’s health system leaders and demonstrate the need for an integrated healthcare system, systematic planning and better coordination to make aging in place for all older adults a reality.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".