New Age[ing]: Small-Scale, Collective, Grassroots Models of Housing for Older Adults
Bibliographic record
Abstract
The elder care system in Ontario is largely failing older adults.Older adults want to age at home, yet public home care systems are underfunded and limited in scope and SECTION I : OLD AGING An Overview of Elder Care in Canada "Are we really going to end up like this?" Items of Note Then to Now Model I : Large-Scale Collective Care Model II : The Culture Change Approach Model III : Aging-in-Place Marginalized Groups in Elder Care Items of Note Structure and Purpose of Section I Definitions: Care: to provide for, look after, take care of 1Elder Care: an encompassing term, used throughout as a term for any method or system that is involved with mitigating age-related issues for older adults Model of Care: an approach that uses a formal, defined methodology in providing care, typically medical but may be social, emotional or holistic in natureThe small-scale, grassroots, collective model of care explored during my thesis work was defined in response to gaps and limitations of the existing field of elder care in Ontario.This model is covered in detail in Section II, but the foundations are built through the research explored during this section.Section I examines three models of care that are used in Ontario; 1) Large-Scale Collective Care; 2) The Culture Change Approach, and; 3) Aging-in-Place.Using these models as case studies, this section investigates both successful practices and where models have failed to meet the needs of older adults or faced limitations.In addition, it introduces the complex political and social context that any new model will need to work within to be successful. Research ScopeThroughout my thesis, but particularly in this section, the sources I draw from give preference to Ontario-based research and statistics rather than a wider-reaching pool of information, such as international sources.The nature of a topic as broad as 'housing and care systems for almost 20% of the Ontario population' means that there are innumerable variables at play, including healthcare systems, housing, finance, municipal services and family systems.2 The preference towards Ontario-based research is an effort to reduce the number of these variables, ensuring that studies are not influenced by systematic differences such as public versus private healthcare systems, governmental differences or 1 Oxford English Dictionary, 2nd Ed., "care."2 Statistics Canada, "Population Estimates on July 1st, by Age and Sex," Statistics Canada (Government of Canada, September 29, 2021), https://
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".