Facilitating engagement of universal school-based digital mental health solutions through user experience: A qualitative exploration
Bibliographic record
Abstract
Digital mental health intervention (DMHI) programs offered in schools present a readily-accessible and flexible means for educating, empowering, and supporting adolescents in maintaining a balanced mental health, especially during uncertain and stressful times such as the COVID-19 pandemic. Recent studies indicate that the effectiveness of DMHI programs in improving students' mental well-being and in preventing from their mental health complications depends on the users' engagement. This study focuses on identifying the user experience factors that can facilitate user engagement with universal school-based DMHI programs (i.e., the DMHI programs delivered to the students regardless of their mental health risks or conditions). To identify said factors, we sought to gain a deeper understanding of perceptions, opinions, and preferences of actual end-users (i.e., the adolescents) regarding their experiences with both digital and non-digital mental health resources. Specifically, interviews were conducted with two participant groups to uncover the reasons that could lead the adolescents to better engage with school-based DMHI programs, as well as the shortcomings that could prevent that from happening: (a) adolescent users who had either a high or a low level of engagement with universal DMHI programs of a specific school-based digital mental health solution; and (b) adolescents who had voluntarily used non-digital or non-school-based digital mental health resources for purposes other than treatment. Through a thematic analysis of interview data, the most important (or primary) and the additionally desirable (or secondary) factors that could lead to a higher engagement level for school-based DMHI programs were identified. Lastly, using the evidence gathered from our interviews, specific recommendations are proposed that could help in targeting each identified engagement factor and in increasing the likelihood that school-based DMHI programs achieve their desired outcome for high school students.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".