“When you are working in this environment, you’re more likely to get sick”: Mapping Care Relationships in LTC
Bibliographic record
Abstract
Abstract The pandemic has shone a light on problems within the long-term care (LTC) sector. As was true prior to COVID-19, many of the present issues in LTC can be traced to challenging working conditions, such as persistent understaffing of care workers. Working short-staffed means rushing through care, while only satisfying the most basic bodily needs of the resident. This presentation shares early findings from a thematic analysis of interviews conducted with seven care workers as part of the “Mapping Care Relationships” stream of the Seniors –Adding Life to Years (SALTY) project, a pan-Canadian research program that maps how promising approaches to care relationships are organized and experienced in LTC. The purpose of the analysis was to understand how short-staffing is affecting the formation and preservation of meaningful staff-resident relations, and what the impact is on quality of care. Two overarching themes emerged: 1) a relationship between time and work-place illness, injury and violence; 2) a relationship between care worker autonomy and resident quality of care. When working conditions do not support workers in voicing and/or addressing challenges they experience in the workplace, whether this results from understaffing or hierarchical power structures, care workers’ ability to deliver even basic care is jeopardized, and resident and worker health and wellness are placed at risk. Themes are discussed in the context of COVID-19 in light of responses to outbreaks in LTC that have reduced the availability of care workers, family visitors and volunteers, and emphasized top-down and even militarized approaches to care management.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".