ASSESSING THE IMPACT OF COVID-19 ON THE HEALTH AND WELLNESS OF THE LONG-TERM CARE WORKFORCE IN RURAL AND NORTHERN AREAS
Bibliographic record
Abstract
Abstract There is growing recognition that the mental health and wellbeing of the LTCF workforce have been disproportionately impacted by COVID-19. Therefore, we sought to describe the experiences and challenges LTCF employees faced during COVID-19 in rural and northern communities and highlight factors affecting their ability to be resilient and provide high quality care. We conducted 53 qualitative interviews using zoom with LTCF care providers (care aides, nurses, social workers), staff (food service workers, recreation providers), and management between November 2021 and February 2021. Data was transcribed and thematic analysis undertaken. We will describe participants experiences stratified by LTCF employee type and highlight similarities and differences in participants experiences across geography and facility type (freestanding vs. co-located in hospital) and describe factors affecting well-being, job satisfaction, and retention. We will share an inventory of programs and strategies participants found useful to mitigate negative effects on their mental health and well-being.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".