Disability acceptance and depressive symptoms: the moderating role of social support
Bibliographic record
Abstract
PURPOSE: This study aims to estimate (a) the relationship between disability acceptance and depressive symptoms, and (b) how the quality and quantity of social support might moderate the link between disability acceptance and depressive symptoms. MATERIALS AND METHODS: The data for this study included information from 5165 individuals with disability who participated in 3 waves of the Disability and Life Dynamic Panel spanning years 2018 to 2020. This study employed fixed effects models to estimate the association between disability acceptance and depressive symptoms. Interaction models were used to assess the moderating effects of both the quantity and quality of social support. RESULTS: A lower acceptance of disability was positively associated with depressive symptoms. Moreover, both the quantity and quality of social support were associated with a decrease in depressive symptoms. Only the quality of social support played a significant role in moderating the relationship between disability acceptance and depressive symptoms. CONCLUSION: A lower acceptance of disability increases depressive symptoms in individuals with disabilities. This study underscores the need for interventions to focus on enhancing the quality of social support to mitigate the link between disability acceptance and depressive symptoms.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".