The National Standard of Psychological Health and Safety in the Workplace: A Psychometric and Descriptive Study of the Nursing Workforce in British Columbia Hospitals
Bibliographic record
Abstract
BACKGROUND: In 2013, the Mental Health Commission of Canada created a National Standard that includes 13 workplace factors associated with employee mental health. PURPOSE: This study (a) examined the psychometric properties of Guarding Minds at Work (GMW), the instrument used to measure the Standard's 13 workplace factors and (b) assessed BC nurses' workplace risk factors. METHODS: A province-wide survey study of 3,077 direct care nurses working in acute care settings was conducted. RESULTS: Subscale internal consistencies were acceptable. For most items, the original alphas were greater than the alpha-if-item-deleted. All corrected item-subtotal correlations were moderate to high. The 13-factor structure showed an adequate model fit based on absolute fit indices (SRMR = 0.057 and RMSEA = 0.054) but the relative fit indices were lower than the recommended cutoff (CFI = 0.827 and TLI = 0.815). Nurses identified nine of the 13 GMW factors as serious or significant concern in their workplace. CONCLUSIONS: The findings were consistent with a plethora of evidence pointing to shortcomings in nurses' work environments. This was the first study partially supporting the reliability and validity of the GMW. More work is required to refine the GMW and gain a better understanding of its psychometric properties.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".