Investigating How People Who Self-harm Evaluate Web-Based Lived Experience Stories: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: The positive and negative effects of interacting with web-based content on mental health, and especially self-harm, are well documented. Lived experience stories are one such type of static web-based content, frequently published on health care or third-sector organization websites, as well as social media and blogs, as a form of support for those seeking help via the web. OBJECTIVE: This study aimed to increase understanding about how people who self-harm engage with and evaluate web-based lived experience stories. METHODS: Overall, 4 web-based focus groups were conducted with 13 people with recent self-harm experience (aged 16-40 years). In total, 3 example lived experience stories were read aloud to participants, who were then asked to share their reactions to the stories. Participants were also encouraged to reflect on stories previously encountered on the web. Data were analyzed thematically. RESULTS: Overall, 5 themes were generated: stories of recovery from self-harm and their emotional impact, impact on self-help and help-seeking behaviors, identifying with the narrator, authenticity, and language and stereotyping. CONCLUSIONS: Lived experience stories published on the web can provide a valuable form of support for those experiencing self-harm. They can be motivating and empowering for the reader, and they have the potential to distract readers from urges to self-harm. However, these effects may be moderated by age, and narratives of recovery may demoralize older readers. Our findings have implications for organizations publishing lived experience content and for community guidelines and moderators of web-based forums in which users share their stories. These include the need to consider the narrator's age and the relatability and authenticity of their journey and the need to avoid using stigmatizing language.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".