Review of layperson screening tools and model for a holistic mental health screener in lower and middle income countries
Notice bibliographique
Résumé
Abstract Background The needs of people diagnosed with Mental Neurological and Substance-Use (MNS) conditions are complex including interactions physical, social, medical and environmental factors. Treatment requires a multidisciplinary approach including health and social services at different levels of care. However, due to inadequate assessment, services and scarcity of human resource for mental health, treatment of persons diagnosed with MNS conditions in many LMICs is mainly facility-based pharmacotherapy with minimal non-pharmacology treatments and social support services. In low resource settings, gaps in human resource capacity may be met using layperson health workers. A layperson health working is one without formal mental health training and may be equivalent to community health worker (CHW) or less cadre in primary health care system. Objectives This study reviewed layperson mental health screening tools for use in supporting mental health in developing countries, including the content and psychometric properties of the tools. Based on this review this study proposes recommendations for the design and effective use of layperson mental health screening tools based on the Five Pillars of global mental health. Methods A systematic review was used to identify and examine the use of mental health screening tools among laypersons supporting community-based mental health programs. PubMed, Scopus, CINAHL and PsychInfo databases were reviewed using a comprehensive list of keywords and MESH terms that included mental health, screening tools, lay-person, lower and middle income countries. Articles were included if they describe mental health screening tools used by laypersons for screening, delivery or monitoring of MNS conditions in community-based program in LMICs. Diagnostic tools were not included in this study. Trained research interviewers or research assistants were not considered as lay health workers for this study. Results There were eleven studies retained after 633 were screened. Twelve tools were identified covering specific disorders (E.g. alcohol and substance use, subcortical dementia associated with HIV/AIDS, PTSD) or common mental disorders (mainly depression and anxiety). These tools have been tested in LMICs including South Africa, Zimbabwe, Haiti, Malaysia, Pakistan, India, Ethiopia and Brazil. The included studies show that simple screening tools can enhance the value of laypersons and better support their roles in providing community-based mental health support. However, most of the layperson MH screening tools used in LMICs do not provide comprehensive information that can inform integrated comprehensive treatment planning and understanding of the broader mental health needs of the community. Conclusion Developing a layperson screening tools is vital for integrated community-based mental health intervention. This study proposed a holistic framework which considers the relationship between individual’s physical, mental and spiritual aspect of mental health, interpersonal as well as broader contextual determinants (community, policy and different level of the health system) that can be consulted for developing or selecting a layperson mental health screening instrument. More research are needed to evaluate the practical application of this framework.
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 | 0,012 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,017 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».