Experiences of Using the Digital Support Tool MeeToo: Mixed Methods Study
Bibliographic record
Abstract
Background Digital peer support is an increasingly used form of mental health support for young people. However, there is a need for more research on the impact of digital peer support and why it has an impact. Objective The aim of this research is to examine young people’s experiences of using a digital peer support tool: MeeToo. After the time of writing, MeeToo has changed their name to Tellmi. MeeToo is an anonymous, fully moderated peer support tool for young people aged 11-25 years. There were two research questions: (1) What impacts did using MeeToo have on young people? (2) Why did using MeeToo have these impacts on young people? Methods A mixed methods study was conducted. It involved secondary analysis of routinely collected feedback questionnaires, which were completed at two time points (T1 and T2) 2-3 months apart. Questionnaires asked about young people’s (N=876) experience of using MeeToo, mental health empowerment, and well-being. Primary data were collected from semistructured interviews with 10 young people. Results Overall, 398 (45.4%) of 876 young people completed the T1 questionnaire, 559 (63.8%) completed the T2 questionnaire, and 81 (9.2%) completed both. Descriptive statistics from the cross-sectional analysis of the questionnaires identified a range of positive impacts of using MeeToo, which included making it easier to talk about difficult things, being part of a supportive community, providing new ways to help oneself, feeling better, and feeling less alone. Subgroup analysis (paired-sample t test) of 58 young females who had completed both T1 and T2 questionnaires showed a small but statistically significant increase in levels of patient activation, one of the subscales of the mental health empowerment scale: time 1 mean=1.83 (95% CI 1.72-1.95), time 2 mean=2.00 (95% CI 1.89-2.11), t59=2.15, and P=.04. Anonymity and the MeeToo sense of community were identified from interviews as possible reasons for why using MeeToo had these impacts. Anonymity helped to create a safe space in which users could express their feelings, thoughts, and experiences freely without the fear of being judged by others. The MeeToo sense of community was described as a valuable form of social connectedness, which in turn had a positive impact on young people’s mental health and made them feel less isolated and alone. Conclusions The findings of this research showed a range of positive impacts and possible processes for young people using MeeToo. Future research is needed to examine how these impacts and processes can be sustained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".