The Sociodemographic Factors Related to Disability of Applicants of Welfare Benefits in Greece: A Cross-Sectional Survey Based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0
Bibliographic record
Abstract
INTRODUCTION: The aim of the present study was to report on the prevalence of disability and its association with sociodemographic factors among welfare benefit applicants in Greece. The study also compared the disability scores between different health conditions using the WHODAS 2.0 (12-item version), a biopsychosocial-model-based measure. METHODS: The Greek WHODAS 2.0, 12-item version, was administered by interview. A three-member medical committee assessed the medical records of the applicants and assigned a disability percentage based on the biomedical measure of disability percentage determination (Barema scale). RESULTS: The majority of the participants were female (56.65%). Certain health conditions were presented more frequently among welfare benefit applicants (mental health disorders and neoplasms). The domains with the highest rate of difficulty were the "participation" and "life activities" domains. Significant differences were found between WHODAS 2.0 and Barema scores for all eight different health condition categories. The factorial ANOVA (8x2) showed a significant interaction effect between health condition category and gender with respect to the WHODAS 2.0 score (F = 19.033, p <.001, η2 = 0.13). The WHODAS 2.0 score was negatively correlated to gender, years of studies, and marital status and positively correlated to age, working status, and the Barema score. The results revealed that male participants with a partner who were younger, had more studies, were actively working, and had a lower Barema score would have lower WHODAS scores. CONCLUSION: Sociodemographic characteristics of welfare benefit applicants are associated with disability levels based on WHODAS 2.0. Certain health conditions, like mental health or neuromusculoskeletal conditions, are associated with higher disability scores. There are differences between the biopsychosocial and the biomedical approaches to disability assessment. The implementation of WHODAS 2.0 may contribute to a better understanding of the lived experience of patients and is a feasible and efficient tool. Combining biomedical and biopsychosocial approaches may enhance the procedures of disability assessment and help in the development of policies that support people with disabilities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".