Creating a Healthy Workplace for New‐Generation Nurses
Bibliographic record
Abstract
PURPOSE: To examine dimensions of the psychosocial work environment that influence the psychological health of new-generation nurses. BACKGROUND: While much work has been done concerning the health of nurses in general, research on the relationship between the nursing work environment and the psychological well-being of new-generation nurses at the start of their careers is limited. DESIGN: A correlational descriptive design was used for this quantitative study. Survey data were collected from new nurses (N=309) whose names were obtained from a provincial licensing registry in Quebec, Canada. FINDINGS: Among new nurses, 43.4% stated that they have a high level of psychological distress. These nurses were significantly more likely to perceive an imbalance between effort expended on the job and rewards received, low decisional latitude, high psychological demands, high job strain, as well as low social support from colleagues and superiors (p < or = 0.05). CONCLUSIONS: Understanding the relationship between the work environment and health as experienced by new-generation nurses is imperative for creating interventions to successfully recruit and retain these young nurses. CLINICAL RELEVANCE: Generation Y nurses in Quebec, faced with high levels of psychological distress because of their exposure to difficult nursing work environments, might leave the profession thereby exacerbating an already salient nursing shortage.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".