Organisational and individual resources as antecedents of older nursing professionals’ organisational commitment: Investigating the mediating effect of the use of selection, optimisation and compensation strategies
Bibliographic record
Abstract
AIM AND OBJECTIVES: To investigate how organisational and individual resources are linked to older (50+) nursing professionals' organisational commitment, and to examine the possible mediating role of the active use of selection, optimisation and compensation (SOC) strategies. BACKGROUND: Many healthcare organisations need to find ways to retain their older nursing professionals due to nursing shortage. DESIGN: To test a set of hypotheses, cross-sectional survey data (n = 396) were used. Data were analysed using correlation analysis and partial least-squares structural equation modelling. STROBE Statement for cross-sectional studies has been followed in this study. RESULTS: The results exhibited that both individual and organisational resources and the active use of SOC strategies were positively associated with older nursing professionals' organisational commitment. The active use of SOC strategies had a partially mediating role in the relationship between individual resource (career management self-efficacy) and organisational commitment. Similarly, career management self-efficacy partially mediated the association between organisational resources (perceived high-involvement work practices) and organisational commitment. CONCLUSIONS: Regarding the retention of older nursing professionals, attention should be paid to both individual and organisational resources and the active use of SOC strategies. RELEVANCE FOR CLINICAL PRACTICE: By providing opportunities to actively use SOC strategies and by paying attention to career management self-efficacy among older nursing professionals, nursing managers may influence the retention of the older nursing workforce. Similarly, supportive organisational practices can support older nursing professionals' career management self-efficacy and their organisational commitment.
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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 | 0.005 | 0.007 |
| 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 | 0.001 |
| 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".