User-Reported Mechanisms of Change on a Suicide Prevention Website: Single-Arm Pragmatic Trial
Notice bibliographique
Résumé
BACKGROUND: Digital platforms can serve as effective interventions for individuals in crisis, those with limited access to mental health resources, or those who prefer web-based support over in-person care. NowMattersNow.org, a web-based platform grounded in dialectical behavior therapy, has been shown to reduce suicidal thoughts and negative emotions. However, little is known about the specific mechanisms that drive these improvements. Identifying the active ingredients that contribute to its effectiveness will help optimize its impact. OBJECTIVE: This study examined the reasons users reported behind reductions in suicidal thoughts and negative emotions after visiting NowMattersNow.org. Specifically, this study sought to determine which reported reasons were associated with greater versus lesser improvements and whether these changes differed across specific subgroups. METHODS: In this single-arm pragmatic trial, data were collected from 3185 respondents who completed a 6-item retrospective survey while visiting NowMattersNow.org. The survey assessed changes in suicidal ideation and emotional distress (ie, intensity upon entering the site vs at the time of survey completion), reasons the website was helpful, and basic nonexclusive demographic information. Cross-tabulations were used to examine the most commonly endorsed reasons for finding the website helpful, while longitudinal regression analyses assessed the statistical significance of changes in suicidal ideation and emotional distress. RESULTS: The majority of participants reported experiencing suicidal thoughts (n=2309, 72.5%) and negative emotions (n=2745, 86.2%) upon arriving at the website, with 52.4% (n=1211) and 55.6% (n=1527) of these individuals, respectively, experiencing reductions in suicidal thoughts and negative emotions after engaging with the site. Regarding the primary aims of the study, the most frequently cited reason for finding NowMattersNow.org helpful was "I learned something" (n=668, 21%), followed by "It distracted me" (n=544, 17.1%) and "I felt less alone" (n=414, 13%). These were also the top 3 reasons reported by LGBTQI individuals, those endorsing alcohol or opioid problems, and those experiencing unusual experiences, though the order varied across groups. Among participants who experienced the largest reduction in suicidal ideation (a 4-point decrease), the most common reasons cited were "It distracted me" (n=5, 29.4%), "I felt less alone" (n=3, 17.6%), and "I felt cared for" (n=3, 17.6%). Similarly, for those with the largest reduction in negative emotions (a 4-point decrease), the most frequently endorsed reasons were "It distracted me" (n=3, 23.1%), "I felt less alone" (n=3, 23.1%), and "I felt cared for" (n=2, 15.4%). CONCLUSIONS: The findings suggest that NowMattersNow.org is an accessible, scalable digital intervention that shows promise for reducing suicidal ideation and emotional distress, particularly in vulnerable populations. Key elements, such as fostering social connectedness, distraction, and educational content, appear to be critical components of its effectiveness, indicating that web-based self-help tools like NowMattersNow.org can provide short-term management of suicidal thoughts and negative emotions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,011 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,006 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,002 |
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 ».