The impact of the work environment on the health-related quality of life of Licensed Practical Nurses: a cross-sectional survey in four work environments
Bibliographic record
Abstract
BACKGROUND: Nurses are known to have negative health outcomes related to their work. While it is acknowledged that nursing work is associated with things like back injuries and burnout, there is limited evidence as to what factors in the work environment contribute to these issues. PURPOSE: The aims of this study were to assess how Licensed Practical Nurses (LPNs) report their Health-related quality of life (HRQoL), and how nurses' health is impacted by their work environment. METHODS: These data used for analysis comes from a cross-sectional survey administered online to all LPNs in Alberta (2018). The survey collected data on the following variables: participant's demographics, the SF-36 HRQoL, Practice Environment Scale of the Nursing Work Index (PES-NW) and the CD-RISC measure of resilience. The beta distribution was used to model HRQoL outcomes. In instances where optimal health (score of '1') was observed then an extended version of beta distribution (called one-inflated beta) was applied. RESULTS: 4,425 LPNs responded to the survey. LPNs (mean age: 40) report lower scores on each SF-36 subscale than the general Canadian population aged 35-44. LPNs who work 'causal' had better physical health, (OR 1.21, CI 1.11-1.32, p = 0.000), and mental health (OR 1.22, CI 1.12-1.30, p = 0.000) than LPNs who work full time, even after controlling for resilience. LPNs' views on the adequacy of staffing and resources in their workplaces have an influence across all dimensions of health. CONCLUSION: This study suggests that improvements in the work environment could positively impact health outcomes and that adequate resourcing could support the nursing workforce.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".