Development of a Mobile App for Occupational Stress Screening Among Female Workers: Protocol for an Exploratory Sequential Design Study
Bibliographic record
Abstract
BACKGROUND: Occupational safety hazards include physical, chemical, ergonomic, biological, and psychological hazards. Technological innovation in screening for occupational stress, especially among female workers, is needed to improve their health and productivity. OBJECTIVE: This research is being conducted to obtain a prediction model of work stress through a questionnaire instrument that includes stressors and symptoms based on the transactional model, as well as measurement of work stress through a mobile app that can be used anywhere. METHODS: The research is conducted in 3 stages: qualitative research, quantitative research (cross-sectional), and mobile app development. Data were collected from companies located in Jakarta, Indonesia. The sample was chosen based on purposive sampling. For the quantitative research (n=430), logistic regression analysis was used. RESULTS: We are developing a work stress screening instrument for female workers, which includes stressors and symptoms based on the transactional model, in the form of a digital platform so that female workers can undertake the examination anywhere without interfering with working hours or home duties. This research was funded in January 2024 and qualitative data collection began in February 2024. Quantitative data were obtained in March 2024; the number of respondents in the qualitative stage was 6, and in the quantitative stage it was 430. The work stress screening app is in the development stage and will be launched at the same time as the data collection is performed so we can examine the respondents' perspectives on the use of the app. CONCLUSIONS: This study analyzes the prediction of work stress to help female workers screen for work stress. Workers who are detected as experiencing work stress will be educated using an algorithm programmed in the app. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/55874.
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".