Contribution of an Online Intervention to Developing Communities of Practice: Mixed Methods Evaluation of an Online Safety Hub to Address Harmful Online Content in Relation to Self-Harm and Suicide
Notice bibliographique
Résumé
Background: Online harm affects many people and has been associated with self-harm and suicidal ideation. Although there is an emerging body of evidence that addressing adverse online experiences should be part of the support offered to people who are at risk of self-harm and suicide, there has been little guidance to date on how this support might be provided and how safe conversations can be had on the subject. A UK charity dedicated to offering emotional support to anyone experiencing mental discomfort, having difficulty coping, or being at risk of suicide developed a digital intervention, the Online Safety Hub (the Hub), to address this shortfall. Objective: The study aimed to evaluate the impact of the Hub on practitioners (people who provide support) and people with lived experiences of suicide and self-harm and to determine what learning environment is best suited to increase and maintain learning in the context of the Hub. Methods: A sequential explanatory mixed methods evaluation comprised a rapid literature review, data collected from people with lived experience (n=6) and practitioners through an analysis of the Hub's activity data, 2 surveys (survey 1: n=45; survey 2: n=368), interviews (n=9), and focus groups (n=7). Surveys were analyzed for descriptive purposes only, and the interview and focus group analyses comprised coding of data and thematic analysis. The study design was informed by a panel of people with lived experience of online harm resulting in either self-harm and/or suicidal ideation. Results: Initially, the evaluation found limited uptake of the Hub. Engagement with the Hub was impeded by a lack of clarity on the part of practitioners as to whether they were the intended audience. The evaluation process prompted the charity to design and deliver webinars to facilitate uptake of the Hub. Practitioners who engaged with the Hub via webinars found the content useful and were able to consider incorporating their learning into practice. The webinars offered a more social learning experience than individual engagement with the Hub, providing a community of practice for people with common interests across diverse organizational settings. Opportunities for shared learning and the supportive nature of the community of practice were valued when learning about the sensitive and difficult topic of online harm in relation to self-harm and suicide. The Hub contributed to awareness-raising and shared learning. Conclusions: Online resources alone may not be sufficient for an intervention to effectively raise awareness and change practice. Social learning facilitated through communities of practice can enhance engagement, uptake, and learning.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,054 | 0,054 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».