ContextMentalQA: Modeling Cultural, Social, and Religious Context in Arabic Mental Health Question Answering
Bibliographic record
Abstract
Question answering (QA) for mental health requires models that attend not only to clinical intent but also to the cultural, social, and religious frames through which individuals articulate distress and seek help. This paper introduces ContextMentalQA, a socio-culturally informed annotation schema and corpus for Arabic mental health questions, designed for multi-label classification across three main categories (Cultural, Social, Religious) and their finer-grained sub-categories. The developed corpus comprises 2,677 patient questions, of which 557 received at least one socio-cultural label, highlighting the predominance of social framing in Arabic mental health discourse. ContextMentalQA is paired with a multi-label classification pipeline based on AraBERT, incorporating imbalance-aware optimization, semisupervised augmentation via high-confidence pseudo-labeling, and adaptive per-class threshold calibration. Empirical analyses demonstrate that incorporating pseudo-labeled data yields consistent improvements across standard metrics, with reduced label-wise error and stronger performance to underrepresented categories. The proposed schema, dataset, and baseline models provide a foundation for developing Arabic mental health QA systems that are linguistically accurate, culturally grounded, and socially responsive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".