The relationship between quality of work life and location of cross-training among obstetric nurses in urban northeastern Ontario, Canada: A population-based cross sectional study
Bibliographic record
Abstract
OBJECTIVES: The purpose of this mixed methods study was to examine the quality of work life of registered nurses working in obstetrics at 4 hospitals in northeastern Ontario and explore demographic and occupational factors related to nurses' quality of work life (QWL). MATERIAL AND METHODS: A stratified random sample of registered nurses (N = 111) selected from the 138 eligible registered nurses (80.4%) of staff in the labor, delivery, recovery, and postpartum areas at the 4 hospitals participated. Logistic regression analyses were used to consider QWL in relation to the following: 1) demographic factors, and 2) stress, employment status and educational attainment. RESULTS: In the logistic regression model, the odds of a higher quality of work life for nurses who were cross trained (nurses who can work across all areas of obstetrical care) were estimated to be 3.82 (odds ratio = 3.82, 95% confidence interval: 1.01-14.5) times the odds of a higher quality of work life for nurses who were not cross trained. CONCLUSIONS: This study highlights a relationship between quality of work life and associated factors including location of cross-training among obstetrical nurses in northeastern Ontario. These findings are supported by the qualitative interviews that examine in depth their relationship to QWL. Given the limited number of employment opportunities in the rural and remote regions, it is paramount that employers and employees work closely together in creating positive environments that promote nurses' QWL.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".