Associations between level of services integration and nurses’ workplace well-being
Bibliographic record
Abstract
BACKGROUND: To respond better to population needs, in recent years Quebec has invested in improving the integration of services and care pathways. Nurses are on the front lines of these transformation processes, which require them to adopt new clinical practices. This updating of practices can be a source of both satisfaction and stress. The aim of this study was to gain a better understanding of the relationship between the transformation processes underlying services integration and nurses' workplace well-being. METHOD: This study was based on a descriptive cross-sectional correlational design. The target population included all nurses working in four care pathways in a Quebec healthcare establishment: palliative oncology services, mental health services, autonomy support for the elderly, and chronic obstructive pulmonary disease. In all, 107 nurses took part in the study and completed a questionnaire sent to them. Hierarchical linear regression analyses were used to examine the relationship between level of integration, measured using the Development Model for Integrated Care; nurses' perceptions of organizational change, measured on four dimensions (challenge, responsibility, threat, control); and nurses' workplace well-being, measured on three dimensions (negative stress, positive stress, satisfaction), as defined by the Flexihealth model. RESULTS: Nurses in the palliative oncology care pathway, which was at a more advanced level of integration, presented a lower negative stress level and a higher positive stress level than did nurses in other care pathways. Their mean satisfaction score was also higher. More advanced integration was associated with nurses' feeling less threatened, as well as improved workplace well-being. The perception of threat appeared to be a significant mediating variable in the relationship between level of integration and well-being. CONCLUSION: The association observed between level of services integration and workplace well-being contributes to a better understanding of nurses' experiences in such situations. These results provide new perspectives on interventions that could be implemented to remedy the potential negative consequences of these types of transformations.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".