Young people’s perspectives on engaging with an online mental health program: a focus group study (Preprint)
Bibliographic record
Abstract
Background: Despite accessibility and clinical benefits, open access trials of self-guided digital health interventions (DHIs) for young people have been plagued by high drop-out rates, with some DHIs recording completion rates of less than 3%. Objective: The aim of this study was to explore how young people motivate themselves to complete an unpleasant task and to explore perceived motivators and demotivators for engaging with a DHI. Methods: In this qualitative research study, 30 children and adolescents aged between 7 and 17 years were recruited to participate in 7 focus groups conducted over a 3-month period. Focus group activities and discussions explored sources of motivation to complete tasks and engage in a hypothetical 6-week DHI for anxiety. Results: Children (aged 7-11 years) reported greater reliance on external motivators such as following parent instruction to complete unpleasant tasks, while adolescents (aged 12-17 years) reported greater internal motivation such as self-discipline. Program factors, such as engaging content, were the most commonly mentioned motivators for engaging with a DHI across both age groups. After that, internal sources of motivation were most commonly mentioned, such as perceived future benefits. External factors were the most commonly mentioned demotivators across all ages, with time commitment being the most frequently mentioned. Conclusions: The study's findings have implications for enhancing adherence in future DHIs targeted to children and adolescents. Recommendations include the need for supportive parental involvement for children, while adolescents would likely benefit from mechanisms that promote autonomy, establish a supportive environment, and align with personal interests and values. Belief that a DHI will provide short-term benefits is important to both children and adolescents, as well as having confidence that future benefits will be realized.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".