Nurses' work experiences 5 years after hospital merger in the province of Quebec/Canada—An exploratory qualitative study
Bibliographic record
Abstract
In recent years, healthcare organisations in North America have undergone major structural changes. In particular, the province of Quebec in Canada adopted a reform in 2015 which led to the merging of healthcare organisations into centralised regional administrations (the 'CISSS'). As research indicates negative impacts of mergers on patient outcomes and difficulties for the nursing work group in particular, the present paper aims to answer calls for more research about the long-term effects of major organisational changes on nurses' professional practice and well-being. We used an exploratory qualitative research design and report on data collected from 42 nursing professionals, ranging from clinical nurses, nurse practitioners, to head nurses and nursing advisors. Drawing on the job demands-resources model and the person-environment fit theory, our findings yield three main conclusions regarding the state of nursing practice 5 years after the 2015 reform: (1) emergence of a new demand for work harmonisation; (2) growing gaps in the nursing practice environment across departments; (3) evidence of a structural disempowerment of the nursing practice in healthcare organisations. There is hope that a vast project for practice harmonisation initiated and led by local senior nursing advisors will bring about positive outcomes for the nursing practice, and nurses' overall working conditions in the province.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".