A cross-sectional study on workplace experience: a survey of nurses in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Nurses play a significant role in healthcare systems. Their workplace experience can have an impact not only on nurses themselves, but also on patients and organizations, particularly in terms of quality of care and performance. Despite the importance of this experience, it remains an ambiguous concept with varying interpretations. Current studies do not fully capture its complexity, as its multiple dimensions are often considered in isolation. As such, developing a portrait of nurses' workplace experience that integrates its multiple dimensions can provide decision-makers with better indications regarding what levers can be mobilized to generate positive results for nurses, patients, and organizations. AIM: To identify profiles of nurses' workplace experience in Quebec, Canada. DESIGN: Cross sectional. METHODS: In April 2017, 891 nurses participated in this study by completing a self-administered questionnaire. Four dimensions of nurses' workplace experience were measured: resources available to them in their workplace, personal resources, demands (psychological and physical) placed on them, and outcomes associated with their work. Descriptive and factorial analyses were performed. RESULTS: Three profiles of nurses' workplace experience emerged from the factorial analyses: nurses in distress, nurses in moderately positive situations, and nurses in positive situations. CONCLUSION: The study identified profiles of nurses' workplace experience that were differentiated based on nurses' access to workplace resources, the demands of their work, and outcomes. Healthcare managers can use the results to improve the quality of nurses' workplace experience by improving access to structural work resources and alleviating psychological demands.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".