CHILD IMMIGRANT POST-MIGRATION MENTAL HEALTH: A QUALITATIVE INQUIRY INTO CAREGIVERS’ PERSPECTIVES
Bibliographic record
Abstract
Immigrant families and their children experience isolation after migration to Canada. Inadequate income, unemployment, and underemployment have all been identified as primary challenges to the mental health of immigrant families. This study qualitatively explored the perceptions of six Middle Eastern immigrant caregivers regarding their children’s post-migration mental health. The research was situated in the constructivist paradigm, and qualitative descriptive design was used to explore participant experiences. Interviews were conducted in English with three Farsi-speaking and three Arabic-speaking caregivers. Reflexive thematic analysis was performed. Three themes were developed: (a) parents feel their children are isolated and lonely; (b) caregivers’ limited access to resources impacts their children’s mental health; and (c) community connections enhance families’ mental health. Findings suggest children’s experiences with family separation and exposure to racism contributed to children’s loneliness. Children’s isolation was exacerbated by caregivers’ limited access to resources to support their children’s transition into Canada. Caregivers identified social support as an asset to their families’ mental health. This research highlights the importance of culturally responsive health, employment, and education policies, together with programs to provide resources for immigrant families to support their children’s mental health after migration.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".