Measurement tools used to assess individual health assets among refugee populations: a scoping review
Bibliographic record
Abstract
Forced migration is increasing globally, which has detrimental effects on the physical and mental health of refugees, who may face significant challenges accessing healthcare services. However, refugees also possess considerable strengths or assets that can protect against various health challenges. Identifying and strengthening the individual health assets of refugees is critical to promoting their health and mitigating these health challenges. Yet, there is a paucity of data on refugees' individual health assets, including tools to measure them. Therefore, this scoping review aimed to identify and summarise the available measurement tools to assess the individual health assets of refugees. We conducted a comprehensive literature search using six electronic databases and a Google search without restrictions on publication dates. We used Arksey and O'Malley's methodological framework approach to streamline the review processes. Forty-one eligible studies were included, from which 28 individual health asset tools were identified. Of these, 11 tools were tested for validity in refugee populations. Among the validated tools, the reliability scores for the measures of individual health asset outcomes, including resilience (Child and Youth Resilience Measure, Wagnild and Young's Resilience Scale, and Psychological Resilience Scale), acculturation (Vancouver Index of Acculturation and Bicultural Involvement Questionnaire), self-esteem (Rosenberg Self-Esteem Scale), and self-efficacy (Generalized Self-Efficacy Scale), ranged from good to excellent. The findings provide guidance for health service planners, humanitarian organisations, and researchers regarding the appropriateness and quality of tools that can be applied to assess individual health assets, which are crucial for designing culturally sensitive asset-based health promotion interventions for refugees.
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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 | 0.040 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.048 | 0.036 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".