Falling Through the Cracks? An Exploration of the Conditions of Care Experienced by Younger Residents Living in Long-Term Care Facilities
Bibliographic record
Abstract
This dissertation examines the situation of younger residents living in long-term care facilities (LTCFs) in Ontario in the decades leading up to the COVID-19 pandemic. Adults under the age of 65 with disabilities and chronic health conditions were impacted by neoliberal processes of long-term care (LTC) reform and the closure of provincial residential institutions for people with disabilities. Gaps in public health and social care associated with these changes led some non-senior adults to turn to LTCFs when their needs were not being met. Very little is known about the situation of younger residents, who comprise less than eight percent of the total resident population in Ontario’s LTCFs. I address this gap by exploring non-senior residents’ “conditions of care”—the practices, interactions, relationships, and structures that make up their everyday experiences living in a LTCF. My study asks: What are the conditions of care for younger residents, do they align with their needs and preferences, and what factors account for the value of and problems with these conditions? Guided by a relational feminist disability perspective, I address these questions by drawing on data from semi-structured interviews with younger residents, direct care workers, and administrators, as well as from a focus group with family members, field notes, and facility-specific documents. I analyze the data as informed by intersecting relations of difference and inequality associated with gender, disability and age, and as situated within a particular set of contexts. My findings demonstrate that for non-senior residents, the promise of LTCFs lies in relational care—the presence of favourable interpersonal care relationships and the practice of care in relational ways. However, relational care is often prevented by the structures of LTC, particularly those associated with public funding inadequacies and the application of strategies associated with new public management (NPM). Addressing these barriers is key to transforming LTCFs into places that are better for younger residents. But LTCFs will not be appropriate until a range of accessible, high quality, public LTC and social services are also made available.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".