Migrant mothers' mental health communication in the perinatal period
Bibliographic record
Abstract
This report brings together findings from a project on perinatal mental health difficulties amongst migrant mothers, funded by the Wellcome Trust (208437/Z/17/Z), and on early motherhood and digital media, funded by the British Academy (SG151884). Both projects involved interviews with mothers in their homes, with a very small number of them interviewed online. Healthcare professionals were sometimes interviewed on phone. Interviews were qualitative and semi-structured and took the form of free-flowing conversations broadly based on a topic guide. Recruitment through informal channels such as social media and word-of-mouth had limited success and participants recruited through this route accounted for around a quarter of the final set of participants. A recruitment agency was commissioned to administer a door-to-door questionnaire to recruit remaining participants who lived across England, covering mainly the Midlands the South of England and Greater London. Mothers came from a wide range of countries of origin largely in South Asia and Africa and a few from continental Europe. There was a mix of first and second generation immigrants in the final sample. A total of 68 mothers participated across the projects. All participants have been assigned pseudonyms. This report uses selective instances of quotes from interviews to illustrate overall findings and themes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".