Developing population interventions with migrant women for maternal-child health: a focused ethnography
Bibliographic record
Abstract
BACKGROUND: Literature describing effective population interventions related to the pregnancy, birth, and post-birth care of international migrants, as defined by them, is scant. Hence, we sought to determine: 1) what processes are used by migrant women to respond to maternal-child health and psychosocial concerns during the early months and years after birth; 2) which of these enhance or impede their resiliency; and 3) which population interventions they suggest best respond to these concerns. METHODS: Sixteen international migrant women living in Montreal or Toronto who had been identified in a previous study as having a high psychosocial-risk profile and subsequently classified as vulnerable or resilient based on indicators of mental health were recruited. Focused ethnography including in-depth interviews and participant observations were conducted. Data were analyzed thematically and as an integrated whole. RESULTS: Migrant women drew on a wide range of coping strategies and resources to respond to maternal-child health and psychosocial concerns. Resilient and vulnerable mothers differed in their use of certain coping strategies. Social inclusion was identified as an overarching factor for enhancing resiliency by all study participants. Social processes and corresponding facilitators relating to social inclusion were identified by participants, with more social processes identified by the vulnerable group. Several interventions related to services were described which varied in type and quality; these were generally found to be effective. Participants identified several categories of interventions which they had used or would have liked to use and recommended improvements for and creation of some programs. The social determinants of health categories within which their suggestions fell included: income and social status, social support network, education, personal health practices and coping skills, healthy child development, and health services. Within each of these, the most common suggestions were related to creating supportive environments and building healthy public policy. CONCLUSIONS: A wealth of data was provided by participants on factors and processes related to the maternal-child health care of international migrants and associated population interventions. Our results offer a challenge to key stakeholders to improve existing interventions and create new ones based on the experiences and views of international migrant women themselves.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".