Community Resilience and Migration: Using Best Evidence Synthesis to Promote Migrant Welfare
Bibliographic record
Abstract
Abstract Community resilience is an important success factor in migration. Many migrants experience behavioral and psychological change due to hardships post-migration. Migrant communities that learn to withstand these hardships are said to be resilient. This paper discusses the different factors that influence migration and the level of community resilience. These factors are: empowerment, social networks, change in the ecological and natural environment and economic factors. By using the best evidence synthesis methodology, this study was able to select the most commonly discussed stressors, practices and desired outcomes. In the thirty-one studies reviewed, the variables that had the highest impact on migrant communities were ranked to help community organizations determine which practices to prioritize in building resilience among migrants. Community actions that back migrant integration, health and well-being, education and support services ranked the highest in this best evidence study. These rankings are important in identifying and prioritizing community developmental opportunities that enhance migrant resilience during the settlement process.
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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 | 0.134 | 0.357 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.036 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".