Perceived strategies for reducing staff-turnover and improving well-being and retention among professional caregivers in Alberta’s continuing-care facilities: A qualitative study
Bibliographic record
Abstract
This qualitative study explored potential factors that lead to turnover and absenteeism and how to improve well-being and retention among professional older-adult-caregivers in Alberta's assisted living (AL) and long-term care (LTC) facilities. Four hundred and forty-seven participants aged 45-54 years were interviewed through a five-item, content-validated open-ended questionnaire. The questionnaire was self-administered in the English language and the soft copy of their responses was transferred into NVIVO version 12 software for coding. A thematic narrative analysis grounded in the "happy productive worker" theory was completed. The main themes were caregivers' perception of the factors affecting their well-being, absenteeism, and turnover, and caregivers' suggestions on ways to improve their well-being and retention. Participants reported that their professional well-being was suboptimal. They suggested that their employers should provide them with the needed social, psychological, and professional support, improve wages and hire more staff to ameliorate absenteeism and turnover rates.
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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 | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".