Examining Quality of Work Life in Atlantic Canadian Long-Term Care Homes: Protocol for a Cross-Sectional Survey Study (Preprint)
Bibliographic record
Abstract
BACKGROUND The Canadian long-term care (LTC) workforce cares for increasingly complex residents. With greater care needs come greater demands. Despite this, LTC staffing and resources are largely unchanged and underresearched over the last decade. The Atlantic provinces are home to the oldest population in Canada, indicating a high need for LTC. The health and well-being of the LTC workforce are critical components of care quality, yet only in Western Canada are such data routinely and systematically collected. Translating Research in Elder Care is a 2-decade research program studying the LTC work environment and has found strong links between the working conditions of LTC staff and resident outcomes. We draw upon their success to generate the evidence needed to understand, support, and manage the LTC workforce in Canada’s four Atlantic provinces. OBJECTIVE This study aims (1) to assess the quality of work life among staff in LTC homes in Atlantic Canada; (2) to examine the effects of the work environment on the quality of work life; and (3) to build capacity for research in the LTC sector in Atlantic Canada among knowledge users, researchers, and trainees. The objective of this paper is to describe the approach needed to examine the quality of work life and health of care staff in LTC homes. METHODS Stratified random sampling will be used to recruit homes in Atlantic Canada. The sampling frame was designed to recruit 25% of the LTC homes in each of the 4 provinces with proportional representation by size; ownership model; and, if applicable, region or language. Key outcome variables include measures of mental health and well-being, quality of work life, intention to leave, workplace context, and missed or rushed care. Primary data will be obtained through structured interviews with care aides and web-based surveys from registered nurses, licensed practical nurses, managers, and allied health providers. Eligible participants were from an LTC home with at least 25 residents, 90% of whom were aged 65 years or older, and had worked in the home for at least 3 months. Multivariate analyses include regression analysis for explaining predictors of quality of work-life outcomes and multilevel modeling for more complex relationships of staff outcomes by provinces and LTC home characteristics. RESULTS Data collection and cleaning are complete as of October 2024 (N=2305). Care aides (n=1338), nurses (n=724), allied health providers (n=154), and managers (n=89) from 53 homes make up the sample. Data analysis is ongoing. Initially, individual reports will present descriptive data for each participating LTC home. Concurrent analysis is planned for publication in peer-reviewed journals. CONCLUSIONS This peer-reviewed research protocol lays the foundation for a comprehensive analysis of the effects of the work environment on the quality of work life of LTC staff in Atlantic Canada. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/66338
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: yes | Observational | medium |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: yes · About a Canadian topic: yes | Observational | high |
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 | 0.033 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".