Validated and culturally specific screening tools and early response programs for the detection and prevention of eating disorders among First Nations peoples in Australia: a scoping review
Notice bibliographique
Résumé
Eating disorders (EDs) are complex mental health conditions that can have severe health consequences, exacerbating the overall disease burden and causing significant economic impacts on healthcare systems. Globally, cultural and societal factors influence the presentation and management of these disorders, necessitating culturally specific approaches to screening and prevention. Among First Nations peoples in Australia, the intersection of historical, cultural, and social factors offers both context and strength in addressing EDs, while also presenting distinct challenges in identification and prevention. This scoping review seeks to map and evaluate existing research on culturally specific and validated screening tools and early response programs tailored for the prevention and detection of EDs among the First Nations population in Australia. The goal is to acquire relevant information and identify gaps that need to be addressed in the literature to develop standardised screening tools and early response programs that are validated, effective, and culturally sensitive. A literature search was conducted through seven online academic databases (PubMed, MEDLINE, PsycINFO, Scopus, Web of Science, CINAHL, and Informit) and included publications from 2009 to 2024. The search strategy focused on ED prevention strategies among First Nations peoples in Australia, with emphasis on screening tools and early response programs. The scoping review found no culturally specific and validated screening tools and early response programs exist that have been specifically developed for First Nations peoples in Australia. After removing the cultural specificity criterion, seven studies were found that utilised six different screening tools and one early response program. Participant demographics across these studies were predominantly Caucasians or non-Indigenous, with First Nations individuals being underrepresented in sample sizes. The findings reveal significant gaps in literature on culturally specific screening tools and early response programs for eating disorders among First Nations peoples in Australia. Existing studies often underrepresent these populations and rely on tools designed for non-Indigenous groups, questioning the generalisability of the effectiveness to First Nations peoples in Australia. This review emphasises the need for future research to adopt culturally competent methodologies led by First Nations peoples. Developing culturally specific tools and programs is crucial for improving health outcomes and achieving equitable mental health within Australian healthcare systems, ensuring that resources are justly distributed. Eating disorders (EDs) are serious mental health conditions that affect people differently based on their cultural background. This research looked at how First Nations peoples in Australia are supported in detecting and preventing eating disorders. We found that most tools and programs used today were designed for non-Indigenous communities and do not consider the unique cultural needs, histories, and strengths of First Nations peoples. Our team reviewed existing studies and found no tools or programs specifically created for First Nations communities. Even when we included general tools, very few First Nations peoples were part of the research - on average, only 6% of participants. This makes it hard to know if these tools or programs work well for their needs. This gap in culturally safe resources can lead to unequal health outcomes. We urge researchers and healthcare systems to partner with First Nations communities to develop tools and programs that respect their cultural values. By centring First Nations voices and leadership, we can create better solutions that truly meet their needs. Together, we can work toward fairer mental health care for all.
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 | 0,031 | 0,126 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,006 |
| Bibliométrie | 0,015 | 0,014 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».