iCare: Insights from the Evaluation of an App for Managing Stress Among Working-Class Indian Women
Bibliographic record
Abstract
Persuasive Technologies (PTs) are widely used for managing stress and improving well-being. PTs could contribute to the effort toward equality by making mental healthcare more accessible, even among underserved communities. However, most existing persuasive applications (apps) focus on designing for people in developed countries. Therefore, to address this gap, this paper presents the evaluation of iCare, a mobile health (mHealth) app for managing stress and improving well-being among an underserved population—the working-class Indian women. Specifically, we combined the power of mobile health and PTs to design the iCare app. To evaluate the effectiveness of iCare for stress management, 30 participants were recruited to use the app for two weeks and completed a post-test questionnaire about their experience followed by an optional interview with 22 participants to uncover additional insights. Quantitative questionnaire data was analyzed using descriptive and inferential statistics, while qualitative interview data was analyzed using a thematic analysis. Results showed that the iCare app was perceived as highly motivational, persuasive, and useful. Also, results show that using the iCare app brought significant positive changes by helping participants to better manage their stress and anxiety. We contribute to HCI research and practice by offering guidelines and insights for designing technologies for people from underserved communities.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".