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Record W4393949266 · doi:10.1037/rep0000561

Estimating anticipatory, immediate, and delayed effects of disability registration on depressive symptoms.

2024· article· en· W4393949266 on OpenAlexaff
Gum‐Ryeong Park, Eun Ha Namkung, Jinho Kim

Bibliographic record

VenueRehabilitation Psychology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsycINFODepressive symptomsPsychologyPsychological interventionCohortCohort studyMental healthPsychiatryClinical psychologyMedicineMEDLINECognition

Abstract

fetched live from OpenAlex

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).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.396
Teacher spread0.380 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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