Improving Mental Health Among Working-Class Indian Women: Insight From An Interview Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mental health applications (apps) show a good potential to help people suffering from a variety of mental illnesses. Most existing apps focus on people from developed countries. Our research contributes by focusing on underserved populations - Indian Working-Class Women. This study aims at creating a stress and anxiety management application for working-class Indian women (people who are a part of the workforce). We conducted a one-on-one interview with 31 working-class Indian women. Participants highlighted household chores and workload as major sources of stress/anxiety for them. The results also highlight several features required in mental health apps by this group including mood tracking features, social community features. Participants highlighted some concerns with existing mental health apps including privacy issues and the high fees charged for premium features. Based on our findings, we discuss design implications for future work in the field of creating interactive mHealth apps.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it