Effectiveness of Computer-Based Psychoeducational Self-Help Platforms for Eating Disorders (With or Without an Associated App): Protocol for a Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Access to psychological health care is extremely difficult, especially for individuals with severely stigmatized disorders such as eating disorders (EDs). There has been an increase in children, adolescents, and adults with ED symptoms and ED, especially following the COVID-19 pandemic. Computer-based self-help platforms (± associated apps) allow people to bridge the treatment gap and receive support when in-person treatment is unavailable or not preferred. OBJECTIVE: The aim of this systematic review is to evaluate the effectiveness of computer-based self-help platforms for EDs, some of which may have associated apps. METHODS: The proposed systematic review will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. This review will report and evaluate the literature concerning the efficacy of self-help platforms for EDs. Articles were obtained from the Ovid MEDLINE, Embase, Global Health, and APA PsycInfo. The inclusion criteria included research with original data and gray literature; research evaluating the efficacy of web-based psychoeducational self-help platforms for EDs; people with an ED diagnosis, ED symptoms, at risk of developing EDs, or from the general population without ED-related behaviors; pre- and post-computer-based ± associated apps intervention clinical outcome of ED symptoms; pre- and post-computer-based ± associated apps intervention associated mental health difficulties; and literature in English. The exclusion criteria were solely guided self-help platforms, only in-person interventions with no computer-based ± associated apps comparison group, only in-person-delivered CBT, self-help platforms for conditions other than eating disorders, systematic reviews, meta-analyses, posters, leaflets, books, reviews, and research that only reported physical outcomes. Two independent authors used the search terms to conduct the initial search. The collated articles then were screened by their titles and abstracts, and finally, full-text screenings were conducted. The Cochrane Risk of Bias 2 tool will be used to assess the risks of bias in the included studies. Data extraction will be conducted, included studies will undergo narrative synthesis, and results will be presented in tables. The systematic review will be submitted to a peer-reviewed journal. RESULTS: The authors conducted a database search for articles published by May 31, 2024. In total, 14 studies were included in the systematic review. Data charting, synthesis, and analysis were completed in Microsoft Excel by the end of July 2024. Results will be grouped based on the intervention stages. The results are expected to be published by the end of 2024. Overall, the systematic review found that computer-based self-help platforms are effective in reducing global ED psychopathology and ED-related behaviors. CONCLUSIONS: Self-help platforms are helpful first-stage resource in a tiered health care system. TRIAL REGISTRATION: PROSPERO CRD42024520866; https://tinyurl.com/5ys2unsw. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60165.
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 | 0,066 | 0,066 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,006 |
| Méta-épidémiologie (sens large) | 0,021 | 0,026 |
| Bibliométrie | 0,011 | 0,010 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,081 | 0,010 |
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 ».