Notice bibliographique
Résumé
Introduction Globally many people are suffering from poor mental health, with conflict-affected populations like refugees particularly affected. Task-sharing between mental health specialists (e.g. psychiatrists, and psychotherapists) and non-specialists (e.g. community workers, social workers, and lay health workers) can strengthen the health workforce and enhance access to psychological care. Despite their potential, task-sharing interventions in global mental health have rarely been successfully implemented at scale. The aim of this thesis was to gain insights into how novel psychological interventions for refugees can be embedded into existing systems, and ultimately contribute to health system strengthening and improved refugee’ mental health. Methodology All case studies (chapters 5-7) were guided by the same conceptual framework (chapter 3) and used semi-structured interviews as primary method of data collection. Rapid appraisals, using a health responsiveness framework, were conducted in eight countries hosting Syrian refugees (chapter 4). This PhD research was embedded in the STRENGTHS study, which ran from 2017 to 2022. Results Chapter 3 presents the conceptual framework. First, a case was made for using a “system innovation perspective”. Second, key concepts were described and defined. Third, a three-phased plan was presented to operationalise the conceptual framework in our scalability research. Chapter 4 shows findings from the systems analysis. Numerous constraints were found in health system responsiveness towards the MHPSS needs of Syrian refugees in all eight countries part of STRENGTHS: i) Too few appropriate mental health providers and services; ii) Travel-related barriers impeding access to services; iii) Cultural, language, and knowledge-related barriers to timely care; iv) High out-of-pocket costs ; v) Long waiting times for specialist mental health services; and vi) Information gaps. Chapter 5 explores the factors influencing the potential for scaling up Problem Management Plus (PM+) in the Netherlands. Findings suggested that the feasibility of wider implementation will largely depend on whether barriers like stigma, attrition, fragmentation, competition, legal, and financial challenges can be overcome. Formalising the roles of new non-specialist workers was found important, including developing structures for their accreditation and supervision. Three scenarios for institutional anchoring of PM+ were identified. Chapter 6 examines the factors influencing the potential for scaling up PM+ in Jordan. Political momentum was identified as a landscape trend likely facilitating scaling up, while predicted reductions in financial aid was regarded as a constraint. The medicalised approach to mental health, stigma, and gender were reported culture-related barriers for scaling up PM+. Using non-stigmatising language, and offering different modalities, childcare options, and sessions outside of working hours were suggestions to reduce stigma, accommodate individual preferences, and increase the demand for PM+. In relation to structure, the feasibility of scaling up PM+ largely depends on the ability to overcome legal barriers, limitations in human and financial resources, and organisational challenges. Chapter 7 examines the scalability of Step-by-Step (SbS) in Egypt, Germany, and Sweden. Contextual factors were: increasing use of e-health; reduced contact during the COVID-19 pandemic; and political instability. Factors related to culture: perceived need and acceptability of the innovation. Factors related to structure: financing; regulations; accessibility; competencies of e-helpers; and quality control. Factors related to practice were barriers in initial and continued engagement of end-users. Nineteen powerful stakeholders were identified and several context-specific integration scenarios were developed. Conclusions This in-depth research has improved knowledge on factors influencing the potential for scaling up task-sharing and digital psychological interventions for refugees in different countries. The interactions between an innovation, potential adoptive systems, and its wider context are complex and difficult to predict. The factors influencing scalability identified through the case studies and the developed integration scenarios will be an important starting point for actors involved in taking such innovations to scale.
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 | 0,021 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,006 |
| Communication savante | 0,009 | 0,008 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,026 | 0,004 |
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 ».