Perceived organizational membership and the retention of older workers
Bibliographic record
Abstract
Abstract Drawing on the perceived organizational membership theoretical framework and the group‐value justice model, we developed and tested a model predicting older workers' intention to remain with their organization. We hypothesized that human resource practices targeted to older workers would be related to perceived insider status through how older workers perceived their supervisor managed these practices (perceived procedural and interpersonal justice). We also hypothesized that perceived insider status would mediate the relationship between perceived contribution and intention to remain. We conducted two studies to test the hypothesized model. Study 1 participants ( N = 236) were a diverse group of older workers and Study 2 participants ( N = 420) were older registered nurses. Using structural equation modeling, we found support for the hypothesized model. All of the hypothesized relationships were significant in Study 2 and all except one were significant in Study 1. Older workers will want to remain a member of their organization when their organization engages in practices tailored to the needs of older workers, their supervisor implements these practices fairly, and their organization conveys that it values the contribution of its older workers thereby fostering a strong sense of belonging. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".