Experiences of Using the Digital Support Tool MeeToo: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND Evidence suggests that digital peer support tools could improve mental health, social functioning, and quality of life amongst young people, particularly during the COVID-19 pandemic. However, there is a need for more research on the impact of digital peer support, young people’s experiences of digital peer support, how it can be improved, and the barriers and facilitators to engaging with digital peer support. OBJECTIVE The aims of this evaluative study of MeeToo were to investigate: (1) what young people thought of MeeToo, (2) how and when young people used MeeToo, (3) young people’s experiences of using MeeToo, (4) the impact of MeeToo on young people’s connection to others, wellbeing, and their ability to support others. METHODS A mixed methods evaluation design was used consisting of a qualitative and quantitative methodology. Semi-structured interviews with n = 10 young users were analysed using thematic analysis. Routinely collected activity data and experience and outcome questionnaire data were analysed from a n = 398 in the cross-sectional sample and n = 559 in the longitudinal sample. RESULTS Two main themes were identified in the qualitative and quantitative analyses: “I don't like talking to people I know, but having an anonymous platform…is a really good thing”, and the MeeToo sense of community. There was also change over time in the longitudinal sample suggesting that young people using MeeToo increasingly learned new ways to help themselves and seek support. CONCLUSIONS Overall young people support the concept of MeeToo and have had a positive experience of the app. Young people welcome MeeToo as a safe space for both personal reflection, peer support and gaining insight in their mental health and wellbeing.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".