Seeking resources: predicting retirees' return to their workplace
Bibliographic record
Abstract
Purpose This study aims to employ a resource‐oriented theoretical perspective to examine retirees' desire to return to their former organization. Design/methodology/approach Using a cross‐sectional field study design, data were collected from 243 retirees under 65 years of age who had been retired from a career job less than ten years. Findings Regression results indicate that retirees who had experienced financial and pervasive role loss as well as retirees who perceived a higher fit with their former organization and the availability of desired job role options expressed significantly greater interest in returning. Retirees who experienced gains in leaving work as well as gains in their life satisfaction following retirement reported significantly less interest in returning to their former organization. Research limitations/implications The cross‐sectional design and self‐report data create a potential for bias. Even though the findings are based on respondents' “interest” in returning to their former organization, it is not known if they actually did return. Practical implications Programs should focus on creating an environment that values older workers, and provides them with opportunities such as mentoring other workers. Social implications Policy changes are needed to ensure that returning to work following retirement results in resource gains and not resource losses. Originality/value This study uses resource theory with a diverse sample of retirees and considers their desire to return to their original employers, thus adding value to human resources and management who wish to retain or re‐engage their own knowledgeable retirees.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".