Understanding contributors to quality of care in long-term care and changes under COVID-19
Bibliographic record
Abstract
Abstract Nursing home facilities are responsible for providing care for some of the most vulnerable groups in society, including the elderly and those with chronic medical conditions. In times of crisis, such as COVID-19 or other pandemics, the delivery of ‘regular’ care can be significantly impacted. In relation to COVID-19, there is an insufficient supply of personal protective equipment (PPE) to care for residents, as PPE not only protects care staff but also residents. Nursing homes across the United States and Canada have also taken protective measures to maximize the safety of residents by banning visitors, stopping all group activities, and increasing infection control measures. This presentation shares a research protocol and early findings from a study investigating the impact of COVID-19 on quality of care in residential long-term care (LTC) in the Canadian province of New Brunswick. This study used a qualitative description design to explore what contributes to quality of care for residents living in long-term care, and how this could change in times of crisis from the perspective of long-term care staff. Interviews were conducted with a broad range of staff at one LTC home. A semi-structured interview guide and approach to thematic analysis was framed by a social ecological perspective, making it possible to include the individual and proximal social influences as well as community, organizations, and policy influencers. Insights gained will improve the understanding of quality of care, as well as potential barriers and facilitators to care during times of crisis.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.001 | 0.004 |
| 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".