La Maison Bleue: Strengthening resilience among migrant mothers living in Montreal, Canada
Bibliographic record
Abstract
INTRODUCTION: La Maison Bleue is a community-based perinatal health and social centre in Montreal that provides services during pregnancy up to age five to families living in vulnerable contexts. The study aimed to describe: 1) the challenges and protective factors that affect the well-being of migrant families receiving care at La Maison Bleue; and 2) how La Maison Bleue strengthens resilience among these families. METHODS: We conducted a focused ethnography. Immigrants, refugees, asylum seekers and undocumented migrants were invited to participate. We collected data from November to December 2017 via semi-structured interviews and participant observation during group activities at La Maison Bleue. Data were thematically analysed. RESULTS: Twenty-four mothers participated (9 interviewed, 17 observed). Challenges to well-being included family separation, isolation, loss of support, the immigration process, an unfamiliar culture and environment, and language barriers. Key protective factors were women's intrinsic drive to overcome difficulties, their positive outlook and ability to find meaning in their adversity, their faith, culture and traditions, and supportive relationships, both locally and transnationally. La Maison Bleue strengthened resilience by providing a safe space, offering holistic care that responded to both medical and psychosocial needs, and empowering women to achieve their full potential towards better health for themselves and their families. CONCLUSION: Migrant mothers have many strengths and centres like La Maison Bleue can offer a safe space and be an empowering community resource to assist mothers in overcoming the multiple challenges that they face while resettling and raising their young children in a new country.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".