The Perpetual Pivot: Understanding Care Partner Experiences in Ontario Long-Term Care Homes during the COVID-19 Pandemic
Bibliographic record
Abstract
Long-term care homes (LTCHs) were impacted during the COVID-19 pandemic. With their ever-changing conditions and restrictions, care partners' roles in LTCHs changed drastically. In this cross-sectional study, an electronic survey was used to examine the experiences of care part-ners who were caring for one or more adults in an Ontario LTCH during the pandemic. The survey was circulated through social media (convenience sample) which produced a convenience sample of 81 caregiver participants. Visit characteristics and a comparison in the quality of care in LTCHs was analyzed before the pandemic as well as during the most restrictive times. Visitation lengths and frequencies, other sources of communication such as phone and video calls, and various types of care provided by caregivers such as personal grooming and personal care all decreased significantly during the pandemic. Care partners also reported that the health of their care recipients decreased significantly during restrictive visitation times. Through thematic analysis, we identified three themes: restrictions and changing LTCH conditions created (1) social isolation and an erosion of connection, (2) a communication breakdown, and (3) a lack of person-centered care. Findings from this research can promote the health and wellbeing of residents and care partners within LTCHs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".