Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".