Experiential meaning of a decent quality of work life for nurse managers in a university hospital
Bibliographic record
Abstract
Objective: Successive reorganizations of healthcare system around the globe have placed enormous pressure on the work of nurse managers (NMs) and this has eroded their quality of work life (QWL). However, little is known about the meaning of NMs’ QWL.Aim: Inspired by Watson’s Human Caring Science perspective, this study aimed to describe and understand the meaning of QWL among NMs working in a affiliated-university hospital.Methods: A descriptive phenomenological method the Relational Caring Inquiry (RCI) was conducted to describe and understand the experiential meaning of QWL. This qualitative method was used to collect and analyze data from two semi-structured interviews with 14 NMs in an affiliated-university hospital in Quebec, Canada.Results: The results have identified the following five Eidos themes to describe and understand the experiential meaning of QWL: (1) actualizing leadership and political skills to improve the quality of nursing care; (2) contextual elements conducive to humanization of the organization; (3) organizational support promoting socioprofessional and personal fulfillment; (4) learning culture within the organization to encourage the development of nursing management skills; and (5) personalized support addressing the specific needs of new NMs. For NMs, the essence of the QWL experience is defined as a socioprofessional emancipation of NMs in their clinical-administrative practice in humanist organizations.Conclusions: Taking an organizational humanization perspective, the results reveal sustainable and practical potential strategies aimed at optimizing QWL implementation programs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".