The Relationship between Wellbeing, Self-Determination, and Resettlement Stress for Asylum-Seeking Mothers Attending an Ecosocial Community-Based Intervention: A Mixed-Methods Study
Bibliographic record
Abstract
Psychosocial support programs have been increasingly implemented to protect asylum seekers' wellbeing, though how and why these interventions work is not yet fully understood. This study first uses questionnaires to examine how self-efficacy, satisfaction of basic psychological needs, and adaptive stress may influence wellbeing for a group of asylum-seeking mothers attending a community-based psychosocial program called Welcome Haven. Second, we explore mothers' experiences attending the Welcome Haven program through qualitative interviews. Analysis reveals the importance of relatedness as a predictor of wellbeing as well as the mediating role of adaptive stress between need satisfaction and wellbeing. Further, attending Welcome Haven is associated with reduced adaptive stress and increased wellbeing, which correspond with the thematic analysis showing that attendance at the workshops fostered a sense of belonging through connection with other asylum seekers and service providers as well as empowerment through access to information and self-expression. The results point to the importance of community-based support that addresses adaptive stress and the promotion of social connection as key determinants of wellbeing. Nonetheless, the centrality of pervasive structural stressors asylum seekers experience during resettlement also cautions that relief offered by interventions may be insufficient in the face of ongoing systemic inequality and marginalization.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".