Intersectionality: Mapping Critical Relations for Quality in Long-Term Care Research
Bibliographic record
Abstract
Abstract Intersectionality is a useful method (Lutz, 2015) for interdisciplinary long-term care (LTC) research to advance a more critical understanding of how experiences of quality are shaped by mutually reproducing social divisions, identities and relations of power that shape LTC. This paper discusses insights from the “Mapping Care Relationships” stream of the Seniors – Adding Life to Years (SALTY) project, a pan-Canadian program of research examining clinical, social and policy perspectives on quality in LTC. “Mapping Care Relationships” mapped how promising approaches to care relationships are organized and experienced in LTC. From January 2018-August 2019 our team of nine researchers conducted rapid ethnographies in eight nursing homes, two in each of four provinces across Canada. We purposively observed and interviewed workers from a wide variety of positions and backgrounds, informed by an intersectionality approach. We traced how promising approaches in person-centred dementia care (PCDC) in particular may reify the subordinated status of care workers (some more than others) and reinforce inequities within LTC systems. In multiple LTC homes, front-line care workers described experiencing physical and emotional harm in care relationships with residents which caused them distress. However, consistent with a PCDC approach, the harm was attributed to ‘behaviours’ clinically symptomatic of dementia. In framing power differentials from a medical perspective, PCDC makes it possible to interpret harmful experiences as 'part of the job’ and something workers should know to expect, prevent, avoid, redirect, or ignore. Lutz, H. (2015). Intersectionality as method. DiGeSt. Journal of diversity and gender studies, 2(1-2), 39-44.
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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 | 0.110 | 0.216 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.028 | 0.025 |
| Science and technology studies | 0.014 | 0.030 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".