Turnover Contemplation in Long-Term Care: Examining Personal and Structural Variables in Canada
Bibliographic record
Abstract
OBJECTIVES: There are high rates of turnover documented among frontline care work staff in long-term residential care (LTC). Turnover has been associated with negative organizational outcomes. This study examined turnover contemplation among LTC workers in several Canadian provinces. DESIGN: A questionnaire including closed- and open-ended questions was sent out to Canadian LTC workers. Workers received a hard copy of the survey through mail and were able to send the hard copy back or complete the survey online. SETTING AND PARTICIPANTS: Canadian LTC workers (N = 347) were surveyed about their work using open- and closed-ended questions. This included demographic information and variables related to working conditions, interactions with supervisors and colleagues, and resident care. METHODS: Data were analyzed in IBM SPSS Statistics. Descriptive statistics and a binary logistic regression were performed using turnover contemplation as the outcome variable. RESULTS: Among Canadian LTC workers, contemplating leaving their current position is significantly and positively associated with lower support from immediate supervisors, working in a non-government-owned facility, and having less autonomy to perform more social care tasks. CONCLUSIONS AND IMPLICATIONS: LTC facilities, and systems more broadly, could improve worker retention rates by improving supervisory support and allowing workers to spend more time meaningfully interacting with patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".