Preretirement resources and postretirement life satisfaction change trajectory: Examining the mediating role of retiree experience during retirement transition phase.
Bibliographic record
Abstract
Successfully adjusting to retirement represents a major challenge for many older workers. Following the temporal unfolding of retirement process (i.e., preretirement, transition, and postretirement phases), the present study draws on the resource-based dynamic model of retirement adjustment to investigate how a diverse set of preretirement personal resources (i.e., physical health, mental health, financial well-being, family support, proactive personality, and general cognitive ability) impact postretirement change trajectory of life satisfaction through the pathway of retirement transition experience (i.e., retirees' subjective experience in terms of how well they are adjusting during the transition phase of retirement immediately after the workforce exit). Using multiwave longitudinal data from 667 Chinese older workers transitioning into retirement collected with a prospective design over 2 years, we found positive effects of the levels of preretirement mental health, financial well-being, family support, proactive personality, and cognitive ability on retirement transition experience. We also found positive effects of the changes in physical health, financial well-being, and family support on retirement transition experience. Retirement transition experience, in turn, was associated with older workers' postretirement change trajectory of life satisfaction. Our findings highlight the importance of the transition phase of retirement, as well as the role of retirement transition experience during this critical phase in explaining the relationships between preretirement resources, in terms of both their levels and changes, and postretirement changes in well-being. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".