Attentes du personnel infirmier de la relève envers l’employeur pour favoriser l’engagement professionnel : un premier jalon pour la conception d’interventions organisationnelles au Québec (Canada)
Bibliographic record
Abstract
Introduction: Poor work conditions jeopardize the quality of nursing jobs and increase turnover and attrition rates, especially for early career nurses and licensed practical nurses. Understanding perspectives of the nursing staff on work engagement is essential to develop innovative and responsive organizational interventions. Objectives: 1- Define the expectations of early career nursing staff with respect to their workplace in order to support work engagement 2- Identify priority actions to support work engagement at organizational level. Methods: A mixed-methods research design based on a conceptual mapping approach was used. Early career nurses and licensed practical nurses (N=14) working in a semi-urban integrated health and social services center in Quebec (Canada) were asked the following question: “What can the workplace do to promote work engagement?” Multidimensional scaling and cluster analysis were used to organize the 49 statements provided by the nursing staff. At the same time, participants prioritized actions by rating the “importance” and “probability of a successful implementation” of each statement. Results: Nursing staffs’ expectations toward their workplace in order to support work engagement were: attractive working conditions, positive leadership, safe working conditions, professional development opportunities, and personalized career path. More specifically, the priority actions identified were: showing respect, providing a healthy environment and avoiding transfer to unwanted departments. Discussion and conclusion: The results suggest several actions to promote work engagement of early career nursing staff working in semi-urban settings in Quebec, including providing human and material resources, implementing a positive organizational culture, and exercising benevolent leadership.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".