Development of a new measure for mental retirement; testing of a three-factor structure of mental retirement in different subgroups
Bibliographic record
Abstract
BACKGROUND: The aim of this study is to develop a new measure for the concept of mental retirement and test the construct validity of the measure. Employees who are 'mentally retired' are present at their work physically, but have already said their goodbyes mentally. Mental retirement has a three-factor structure: developmental proactivity, work engagement and perceived appreciation. METHODS: We use data from employees (N = 867) of five different organizations in the Netherlands. Mental retirement was assessed with 11 items in an online survey. In addition, socio-demographic characteristics like age, level of education and occupation, were measured. Next to tests of internal consistency, a confirmatory factor analysis (CFA) is performed to test the three-factor structure of mental retirement in this population and in different subgroups (age, education, occupation). RESULTS: The internal consistency varies from .80 to .94 for the developmental proactivity scale and the work engagement scale, respectively (appreciation was measured with one item). For the CFA, the three-factor model fits the data adequately. Multiple group analyses also shows equal factor loadings in all subgroups, but the mean levels of mental retirement differ across subgroups. CONCLUSIONS: This study confirms the three-factor model of mental retirement in a general group of employees as well as across different subgroups. However, this study only tested the construct validity. Future research should study validity more extensively and be longitudinal in nature. In addition, the causal chain of antecedent variables to mental retirement and its outcomes should be considered. These studies could also focus on the effects of interventions aiming at preventing or decreasing the level of mental retirement in organizations.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".