Gender is not related to disability acceptance among individuals with disabilities in Korea: A longitudinal observational study.
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: Disability acceptance is an evolving process influenced by personal and contextual predictors, with gender potentially playing a role. This study aimed to examine gender differences in the trajectory of disability acceptance and its predictors among individuals with disabilities in the Republic of Korea (hereafter, Korea). RESEARCH METHOD/DESIGN: We analyzed 4-year longitudinal data (2016-2019) from the Panel Survey of Employment for Persons With Disabilities using multigroup latent growth modeling. The sample consisted of 1,007 men and 1,040 women with disabilities. RESULTS: = .70). Multigroup latent growth modeling results indicated that perceived socioeconomic status, disability-related stress, self-efficacy, self-esteem, and satisfaction with friend relationships significantly predicted disability acceptance over most years, with no gender differences in these predictors. CONCLUSION/IMPLICATIONS: Gender did not predict longitudinal changes in disability acceptance. However, modifiable factors, such as perceived socioeconomic status, disability-related stress, self-efficacy, self-esteem, and satisfaction with friend relationships, were associated with disability acceptance. (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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".