“We have to save him”: a qualitative study on care transition decisions in Ontario’s long-term care settings during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has contributed to a global crisis in long-term care (LTC) with devastating consequences for residents, families and health professionals. In Ontario, Canada the severity of this crisis has prompted some care partners to move residents home with them for the duration or a portion of the pandemic. This type of care transition, from LTC to home care, was highly unusual pre-pandemic and arguably suboptimal for adults with complex needs. This paper presents the findings of a qualitative study to better understand how residents, care partners, and health professionals made care transition decisions in Ontario's LTC settings during the pandemic. METHODS: Semi-structured interviews were conducted with 32 residents, care partners and health professionals who considered, supported or pursued a care transition in a LTC setting in Ontario during the pandemic. Crisis Decision Theory was used to structure the analysis. RESULTS: The results highlighted significant individual and group differences in how participants assessed the severity of the crisis and evaluated response options. Key factors that had an impact on decision trajectories included the individuals' emotional responses to the pandemic, personal identities and available resources. CONCLUSIONS: The findings from this study offer novel important insights regarding how individuals and groups perceive and respond to crisis events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".