“Is it worth having a baby in Canada?”: Understanding the Postpartum Experiences of Immigrant First-time Mothers
Bibliographic record
Abstract
Background: Maternal and infant well-being is a global priority however, in Canada, the cultural needs of immigrant mothers are not always adequately addressed. Despite efforts to support postpartum mothers, there are gaps in the delivery of health care that is tailored to the unique needs of immigrant first-time mothers during the postpartum period. As Canada's immigrant population continues to grow, there is an increasing need for postpartum care that recognizes and addresses the intersectionality of cultural and gendered factors. Research question: What are the postpartum experiences of South Asian immigrant first-time mothers in Ottawa, Ontario? Methodology: Feminist Poststructuralism was used to explore how historical, social, and institutional discourses influenced the postpartum experiences of immigrant first-time mothers. Data collection involved 60-minute semi-structured interviews via telephone and Zoom©. Feminist Poststructuralist Discourse Analysis (Aston, 2016) was utilized to deconstruct textual data to explore power relations, identities, and discourses as they related to postpartum experiences. Findings: Three main themes emerged: Negotiating expectations and realities of postpartum care: ‘You expect a higher level of care’, Balancing South Asian Cultural Identity: ‘I don’t want her to feel left out, and Motherhood: ‘It takes effort, it takes thinking, [it] takes a lot of love’. Conclusion: This study highlights the need for culturally sensitive and gender-inclusive postpartum care. Findings may be used by nurses and other health care providers, to inform culturally safe and gender-inclusive postpartum care practices, guidelines, policies, and research. Addressing the specific needs of South Asian immigrant first-time mothers can enhance the quality of care and support provided.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".