Testing and extending the triple match principle in the nursing profession: a generational perspective on job demands, job resources and strain at work
Bibliographic record
Abstract
AIM: The Triple Match Principle offers insight into the interactive interplay between job demands and job resources in the prediction of work-related strain. The aim of this article was to examine the interplay among job demands, job resources and strain in the nursing profession (the Triple Match Principle) and to gain insight into potential generational differences by investigating generation as a moderator of that interplay. BACKGROUND: No research has been done to evaluate generational differences in the Triple Match Principle. In a context of nursing shortages, it seems important to examine the relevance of the Triple Match Principle with respect to different generations of nurses. DESIGN: Cross-sectional study. METHODS: A total of 1254 public healthcare sector nurses in Quebec, Canada, completed a questionnaire in the autumn of 2010. The questionnaire was used to assess cognitive, emotional and physical job demands and resources; psychological distress; psychosomatic complaints; and turnover intention. RESULTS: The results supported the Triple Match Principle and showed that job resources were more likely to buffer the effect of job demands on strain as the degree of match in qualitative dimension among demands, resources and strain increased (33·3% of triple-match interactions, 22·22% of double-match interactions and 16·67% non-match interactions were significant). Moreover, generation played a key role in this interplay, as it increased the number of significant qualitative interactions among job demands, job resources and strain. CONCLUSIONS: The results underscore the necessity of providing adequate job resources tailored to the specific job demands nurses face, to counteract the negative effects of those demands.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".