Information and communication technology-based interventions for suicide prevention implemented in clinical settings: a scoping review
Notice bibliographique
Résumé
BACKGROUND: A large number of information and communication technology (ICT) based interventions exist for suicide prevention. However, not much is known about which of these ICTs are implemented in clinical settings and their implementation characteristics. In response, this scoping review aimed to systematically explore the breadth of evidence on ICT-based interventions for suicide prevention implemented in clinical settings and then to identify and characterize implementation barriers and facilitators, as well as evaluation outcomes, and measures. METHODS: We conducted this review following the Joanna Briggs Institute methodology for scoping reviews. A search strategy was applied to the following six databases between August 17-20, 2021: MEDLINE, Embase, CINAHL, PsycINFO, Web of Science, and Library, Information Science and Technology Abstracts. We also supplemented our search with Google searches and hand-searching reference lists of relevant reviews. To be included in this review, studies must include ICT-based interventions for any spectrum of suicide-related thoughts and behaviours including non-suicidal self-injury. Additionally, these ICTs must be implemented in clinical settings, such as emergency department and in-patient units. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist to prepare this full report. RESULTS: This review included a total of 75 citations, describing 70 studies and 66 ICT-based interventions for suicide prevention implemented in clinical settings. The majority of ICTs were computerized interventions and/or applications (n = 55). These ICTs were commonly used as indicated strategies (n = 49) targeting patients who were actively presenting with suicide risk. The three most common suicide prevention intervention categories identified were post-discharge follow-up (n = 27), screening and/or assessment (n = 22), and safety planning (n = 20). A paucity of reported information was identified related to implementation strategies, barriers and facilitators. The most reported implementation strategies included training, education, and collaborative initiatives. Barriers and facilitators of implementation included the need for resource supports, knowledge, skills, motivation as well as engagement with clinicians with research teams. Studies included outcomes at patient, clinician, and health system levels, and implementation outcomes included acceptability, feasibility, fidelity, and penetration. CONCLUSION: This review presents several trends of the ICT-based interventions for suicide prevention implemented in clinical settings and identifies a need for future research to strengthen the evidence base for improving implementation. More effort is required to better understand and support the implementation and sustainability of ICTs in clinical settings. The findings can also serve as a future resource for researchers seeking to evaluate the impact and implementation of ICTs.
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 | 0,036 | 0,121 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,009 |
| Bibliométrie | 0,027 | 0,028 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».