From Compliance to Care: Qualitative Findings from a Survey of Essential Caregivers in Ontario Long-Term Care Homes
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic highlighted the importance of the care provided by family members and close friends to older people living in long-term care (LTC) homes. Our implementation science team helped three Ontario LTC homes to implement an intervention to allow family members to enter the homes during pandemic lockdowns. OBJECTIVE: We used a variety of methods to support the implementation, and this paper reports results from an Ontario-wide survey intended to help us understand the nature of the care provided by family caregivers. METHODS: We administered a survey of essential caregivers in Ontario, and a single open-ended question yielded a substantial qualitative data set that we analysed with a coding and theming procedure that yielded 13 themes. FINDINGS: The 13 themes reveal deficiencies in Ontario's LTC sector, attempts to cope with the deficiencies, and efforts to influence change and improvement. DISCUSSION: Our findings indicate that essential caregivers find it necessary to take on vital roles in order to shore up two significant gaps in the current system: they provide psychosocial and emotional (and sometimes even basic) care to residents, and they play a monitoring and advocacy role to compensate for the failings of the current regulatory compliance regime.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".