Variations in Workplace Safety Climate Perceptions and Outcomes Across Healthcare Provider Positions
Bibliographic record
Abstract
EXECUTIVE SUMMARY: Injury rates reported among healthcare practitioners tend to vary depending on position. Nurses and healthcare aides report different rates of injury, which suggests that position and job duties may be key injury antecedents. The outcomes related to workplace safety climate perceptions (e.g., injury rates, job satisfaction, turnover) require reflection to identify antecedents of safety perception. The purpose of this study was to examine workplace safety perceptions and well-being (e.g., stress, job satisfaction) of healthcare practitioners by position. A cross-sectional survey of care teams (e.g., nurses, healthcare aides, allied health professionals) was conducted across three inpatient units. Data (N = 144) were analyzed using hierarchical linear regression and binomial logistic regression to examine the relationship between safety climate and self-reported injuries and ANOVA to determine variations in safety climate perceptions by position. Results indicated that nurses, healthcare aides, and allied health professionals report differing levels of workplace safety climate perceptions. Nurses reported the poorest safety perceptions, lowest job satisfaction, and highest stress, while allied health professionals reported the highest safety perceptions and job satisfaction and the lowest stress. Safety climate perceptions were found to be significantly related to care practitioner reported stress, turnover intent, and job satisfaction. Considering the importance of safety climate perceptions for the well-being of care practitioners, healthcare organizations need to prioritize workplace safety to optimize practitioners' perceptions. This study makes a unique contribution to the safety climate literature by identifying the variation in safety climate perceptions by care practitioner position. Practical implications are offered for enhancing workplace safety perceptions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".