Social Isolation, Loneliness and Health: A Descriptive Study of the Experiences of Migrant Mothers With Young Children (0–5 Years Old) at La Maison Bleue
Bibliographic record
Abstract
Background: Migrant women with young children, including asylum seekers and refugees, have multiple vulnerability factors that put them at increased risk of social isolation and loneliness, which are associated with negative health outcomes. This study explored the experiences of social isolation and loneliness among migrant mothers with children aged 0-5 years as well as their perceptions on possible health impacts. Methods: A qualitative descriptive study was conducted at La Maison Bleue, a non-profit organization providing perinatal health and social services to vulnerable women in Montreal, Canada. Recruitment and data collection occurred concurrently during the COVID-19 pandemic, between November and December 2020. Eleven women participated in individual semi-structured interviews and provided socio-demographic information. Interview data were thematically analyzed. Results: Migrant women in this study described social isolation as the loss of family support and of their familiar social/cultural networks, and loneliness as the feelings of aloneness that stemmed from being a mother in a new country with limited support. Multiple factors contributed to women's and children's social isolation and loneliness, including migration status, socioeconomic circumstances, language barriers, and being a single mother. Women expressed that the COVID-19 pandemic exacerbated pre-existing experiences of social isolation and loneliness. Mothers' experiences affected their emotional and mental health, while for children, it reduced their social opportunities outside the home, especially if not attending childcare. However, the extent to which mothers' experiences of social isolation and loneliness influenced the health and development of their children, was less clear. Conclusion: Migrant mothers' experiences of social isolation and loneliness are intricately linked to their status as migrants and mothers. Going forward, it is critical to better document pandemic and post-pandemic consequences of social isolation and loneliness on young children of migrant families. Supportive interventions for migrant mothers and their young children should not only target social isolation but should also consider mothers' feelings of loneliness and foster social connectedness and belongingness. To address social isolation and loneliness, interventions at the individual, community and policy levels are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".