Estimating anticipatory, immediate, and delayed effects of disability registration on depressive symptoms.
Bibliographic record
Abstract
PURPOSE: This study examines (a) whether disability registration has anticipatory, immediate, and delayed effects on depressive symptoms and (b) how these effects differ by gender. RESEARCH METHOD/DESIGN: Using data from the Korea Welfare Panel Study spanning over 16 waves between 2005 and 2020, this study employed the individual-level fixed effects models to estimate the trajectories of depressive symptoms before and after the registration of physical disability, for a cohort of 20,054 individuals. Furthermore, gender-stratified fixed effects models were used to examine gender differences. RESULTS: Compared to the preregistration reference period (i.e., 4 or more years before disability registration), there was a sustained rise in depressive symptoms leading up to the year of registration, indicating the presence of anticipatory effects. After disability registration, depressive symptoms consistently remained at a statistically higher level than during the initial reference period, with a gradual return to the baseline level of depressive symptoms over time. These anticipatory, immediate, and delayed effects of disability registration were notably more pronounced among men than women. CONCLUSION/IMPLICATIONS: To develop more effective mental health interventions for people with disability, policymakers should consider gendered trajectories of depressive symptoms before and after disability registration. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".