HOW DO WORK HIERARCHIES AND STRICT DIVISIONS OF LABOUR IMPACT CARE WORKERS' EXPERIENCES OF HEALTH AND SAFETY? CASE STUDIESOF LONG TERM CARE IN TORONTO.
Bibliographic record
Abstract
BACKGROUND: According to the Canadian Health Care Association (1), there are 2,577 long-term care ("LTC") facilities across Canada, with the largest proportion (33.4%) located in Ontario. Most studies focus on residents' health, with less attention paid to the health and safety experiences of staff. Given that the work performed in Ontario LTC facilities is very gendered, increasingly racialized, task-oriented, and with strict divisions of labour, this paper explores in what ways some of these factors impact workers' experiences of health and safety. OBJECTIVES: The study objectives included the following research question: How are work hierarchies and task orientation experienced by staff? DESIGN AND SETTING: This paper draws on data from rapid team-based ethnographies of the shifting division of labour in LTC due to use of informal carers in six non-profit LTC facilities located in Toronto, Ontario. METHODS: Our method involved conducting observations and key informant interviews (N=167) with registered nurses, registered practical nurses, personal support workers, dietary aides, recreation therapists, families, privately paid companions, students, and volunteers. Interviews were audio-recorded, transcribed verbatim, and thematically analyzed. For observations, researchers were paired and covered shifts between 7 a.m. and 11 p.m., as well as into the late night over six days, at each of the six sites. Detailed ethnographic field notes were written during and immediately following observational fieldwork. RESULTS: Our results indicate that employee stress is linked to the experiences of care work hierarchies, task orientation, and strict divisions of labour between and among various staff designations. CONCLUSION: Findings from this project confirm and extend current research that demonstrates there are challenging working conditions in LTC, which can result in occupational health and safety problems, as well as stress for individual workers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".