Implementing and improving designated care partner programs in three Ontario long-term care homes
Bibliographic record
Abstract
Long-term care (LTC) residents have an increased risk of social isolation and loneliness, and these risks were exacerbated by pandemic policies that restricted visitors. The designated care partner (DCP) program was introduced in some LTC homes to allow designated family members to safely enter the homes and provide support for residents. We undertook a developmental evaluation (DE) to support the development and implementation of the DCP program in three Ontario LTC homes during the COVID-19 pandemic. Data were collected from 65 staff and DCPs through seven iterations of a DE process. Analysis used directed and inductive coding and theming procedures to create a description of the DCP experience. Themes illustrated the barriers and facilitators to the DCP program and revealed a pervasive deficit of care due to inadequate funding, staff shortages, and an acrimonious relationship between staff and family members. Our project demonstrated a need for additional resources and stronger partnerships between staff and family caregivers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".