Punjabi Immigrant Mothers' Experiences of Postpartum Depression: a Narrative Inquiry
Bibliographic record
Abstract
Postpartum depression can adversely affect not only a woman’s health and well-being, but also the health and development of her infant, as well as her family relationships. Research reveals immigrant women have higher risk factors for postpartum depression. The purpose of this Narrative Inquiry is to give voice, to Punjabi immigrant mothers who have experienced symptoms of postpartum depression. Connelly and Clandinin’s Narrative Inquiry approach was used to explore the experiences of two Punjabi immigrant mothers with self-identified symptoms of postpartum depression. Participants engaged in a narrative interview and an adaptation of the Narrative Reflective Process, a data collection tool that allows creative self-expression and reflection. Womens’ stories were re-constructed and analyzed using Narrative Inquiry’s three levels of justification (personal, practical and social). Findings reveal three key narrative patterns: motherhood, relationships and loneliness, each informed by the narrative thread of immigration. The outcomes of this inquiry suggest that, as healthcare professionals and policy makers, we need to broaden and deepen our understanding of postpartum depression from the immigrant mothers’ perspective, so that we can provide them with a more effective support during this significant time in their lives. Such sensitive and thoughtful care has the ability to improve their well-being and the health of their infant, as well as that of the whole family.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 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".