Factors that influence the quality of worklife of first-line nurse managers in a French Canadian Healthcare system
Bibliographic record
Abstract
Aim(s): This quantitative study sought to explore the factors that influence positively and negatively the quality of work life (QWL) of first-line nurse managers (FLNMs) among healthcare institutions from a humanistic standpoint.Background: In Canada, the public healthcare reforms have had a considerable impact on FLNMs, which could have a negative effect on the FLNMs’ QWL.Method: A quantitative descriptive design was conducted with FLNMs (n = 291) using a Web online survey to identify the factors that influenced favorably and unfavorably the FLNMs’ QWL. A statistical analysis (SPSS software, version 22 for Windows 7) of Quebec’s French Web survey questionnaire highlight was used to conduct this descriptive study.Results: The quantitative results show some significant connections between socio-demographic characteristics, such as age and years of experience, and the choice of factors that affect QWL of FLNMs . The most important favourable factors were the actualization of leadership and political skills to improve quality of nursing, the contextual elements conducive to organizational humanization and the organizational support promoting personal and socioprofessional fulfillment. On the opposite, the main unfavourable factors were the organizational dehumanization, the undesirable working conditions in nursing management and the insufficient coaching of novice nurse managers.Conclusion: Healthcare organizations should develop a QWL program and policies to provide information on nursing management humanistic practices. These findings enable us to provide recommendations in the fourth domains of nursing practice.Implications for nursing management: Healthcare administrators must consider strategies to maximize the QWL of the next generation of FLNMs in healthcare institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".