Using a Safety Planning Mobile App to Address Suicidality in Young People Attending Community Mental Health Services in Ireland: Protocol for a Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Over 700,000 people die by suicide annually, making it the fourth leading cause of death among those aged 15-29 years globally. Safety planning is recommended best practice when individuals at risk of suicide present to health services. A safety plan, developed in collaboration with a health care practitioner, details the steps to be taken in an emotional crisis. SafePlan, a safety planning mobile app, was designed to support young people experiencing suicidal thoughts and behaviors and to record their plan in a way that is accessible immediately and in situ. OBJECTIVE: The aim of this study is to assess the feasibility and acceptability of the SafePlan mobile app for patients experiencing suicidal thoughts and behaviors and their clinicians within Irish community mental health services, examine the feasibility of study procedures for both patients and clinicians, and determine if the SafePlan condition yields superior outcomes when compared with the control condition. METHODS: A total of 80 participants aged 16-35 years accessing Irish mental health services will be randomized (1:1) to receive the SafePlan app plus treatment as usual or treatment as usual plus a paper-based safety plan. The feasibility and acceptability of the SafePlan app and study procedures will be evaluated using both qualitative and quantitative methodologies. The primary outcomes are feasibility outcomes and include the acceptability of the app to participants and clinicians, the feasibility of delivery in this setting, recruitment, retention, and app use. The feasibility and acceptability of the following measures in a full randomized controlled trial will also be assessed: the Beck Scale for Suicide Ideation, Columbia Suicide Severity Rating Scale, Coping Self-Efficacy Scale, Interpersonal Needs Questionnaire, and Client Service Receipt Inventory. A repeated measures design with outcome data collected at baseline, post intervention (8 weeks), and at 6-month follow-up will be used to compare changes in suicidal ideation for the intervention condition relative to the waitlist control condition. A cost-outcome description will also be undertaken. Thematic analyses will be used to analyze the qualitative data gathered through semistructured interviews with patients and clinicians. RESULTS: As of January 2023, funding and ethics approval have been acquired, and clinician champions across mental health service sites have been established. Data collection is expected to commence by April 2023. The submission of completed manuscript is expected by April 2025. CONCLUSIONS: The framework for Decision-making after Pilot and feasibility Trials will inform the decision to progress to a full trial. The results will inform patients, researchers, clinicians, and health services of the feasibility and acceptability of the SafePlan app in community mental health services. The findings will have implications for further research and policy regarding the broader integration of safety planning apps. TRIAL REGISTRATION: OSF Registries osf.io/3y54m; https://osf.io/3y54m. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/44205.
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 | 0,043 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,010 | 0,006 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,007 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,083 | 0,012 |
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 ».