Long-term care transitions during a global pandemic: Planning and decision-making of residents, care partners, and health professionals in Ontario, Canada
Bibliographic record
Abstract
The COVID-19 pandemic appears to have shifted the care trajectories of many residents and care partners in Ontario who considered leaving LTC to live in the community for a portion or the duration of the pandemic. This type of care transition-from LTC to home care-was highly uncommon prior to the pandemic, therefore we know relatively little about the planning and decision-making involved. The aim of this study was to describe who was involved in LTC to home care transitions in Ontario during the COVID-19 pandemic, to what extent, and the factors that guided their decision-making. A qualitative description study involving semi-structured interviews with 32 residents, care partners and health professionals was conducted. Transition decisions were largely made by care partners, with varied input from residents or health professionals. Stakeholders considered seven factors, previously identified in a scoping review, when making their transition decisions: (a) institutional priorities and requirements; (b) resources; (c) knowledge; (d) risk; (e) group structure and dynamic; (f) health and support needs; and (g) personality preferences and beliefs. Participants' emotional responses to the pandemic also influenced the perceived need to pursue a care transition. The findings of this research provide insights towards the planning required to support LTC to home care transitions, and the many challenges that arise during decision-making.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.005 | 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".