Influence of Working Conditions on Satisfaction and Loyalty of Quebec Agency Nurses
Bibliographic record
Abstract
In recent years, the attractiveness of temporary placement agencies for nurses has grown significantly. In a labour scarcity context, it is worth exploring the motivations that encourage nurses to choose temporary work and remain loyal to their agency. Building on the classification of Tan and Tan (2002), four sources of motivation were explored: individual or family incentives, economic incentives, professional motivations and personal preferences. Regarding family motivations, this study was mainly interested in the role of flexible working conditions offered by placement agencies. Concerning economic motivations, we examined the influence of pay conditions. Our investigation of professional motivations centered on agency nurses’ opportunities for skills development. Finally, the role of personal preferences was explored via workload. The results of our study, conducted on two samples, one of 500 nurses working in nursing agencies in Quebec and the other of 99 nurses from two agencies, showed that family and professional development motivations had a positive influence on agency nurses’ satisfaction. In contrast, their loyalty is more closely related to the need for flexible hours, training and skills development, job security and the possibility of choosing one’s assignments. Good salary conditions are not sufficient. Nurses who choose temporary work are motivated by more than a quest for better economic conditions. They also want greater freedom of choice and selfdetermination, and more opportunities for professional development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".