Motivations Toward Using Digital Health and Exploring the Possibility of Using Digital Health for Mental Health in Bangladesh University Students: Cross-sectional Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Digital health is efficacious for the management and prevention of mental health (MH) problems. It is particularly helpful for the young adult population, who appreciate the autonomy digital health provides, and in low-income countries, where the prevalence of MH problems is high but the supply of professionals trained in MH is low. OBJECTIVE: The objectives of this study are 2-fold: to determine whether university students in Bangladesh find using digital health for MH promotion acceptable and to examine motivational factors for using digital health for MH. METHODS: This study used a cross-sectional survey to examine the likelihood that university students in Bangladesh (n=311) would use different forms of digital health platforms for MH promotion and assessed drivers of intention to use and actual use of digital health generally and digital health for MH through the lens of the Technology Acceptance Model. The results provided evidence that the university student population in Bangladesh is likely to use digital health to promote their MH. RESULTS: Social influence (adjusted odds ratio [aOR] 1.68, 95% CI 1.40-2.01; P<.001), ease of use (aOR 1.85, 95% CI 1.35-2.53; P<.001), and perceived usefulness (aOR 4.12, 95% CI 1.79-9.51; P=.001) of digital health were found to be significant drivers of the intention to use general digital health, and having an intention to use digital health (aOR 2.10, 95% CI 1.17-3.78; P=.01) had the greatest influence on actual use of digital health. Social influence (aOR 1.71, 95% CI 1.43-2.04; P<.001), perceived usefulness (aOR 8.92, 95% CI 4.18-19.04; P<.001), and use of general digital health (aOR 2.16, 95% CI 1.18-3.97; P=.01) were associated with higher intention to use digital health for MH. The use of general digital health (aOR 4.19, 95% CI 2.37-7.41; P<.001) was associated with the actual use of digital health for MH, as were greater non-stigma-related barriers to using traditional clinical MH services (aOR 2.05, 95% CI 1.10-3.80; P=.02). CONCLUSIONS: Overall, we see that the use of digital health for MH is acceptable in this population and can be helpful for students who perceive barriers to receiving traditional care. We also gain insight into how to promote the intention to use digital health, which in turn promotes the actual use of digital health.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".