Improving Qualitative Interviews With Newly Arrived Migrant Women
Bibliographic record
Abstract
There is a paucity of literature on how to conduct research with migrants, particularly those who do not speak the host country language, those who are newly arrived, and those who have a precarious immigration status. In qualitative research, interviewing is a common method for obtaining rich data and participants' points of view. Gathering and presenting all perspectives when interviewing vulnerable migrant women on health-seeking behaviors is challenging. In this article, we explore the process of developing and implementing a data collection plan and an interview guide for a study carried out with migrant women to explore the inhibitors/facilitators for following through on professional referrals for postbirth care. Adaptability and careful attention to multiple factors throughout the process are essential to maximizing participation and enhancing the trustworthiness of the data. Appropriate health policy and care delivery can only originate from health research with diverse migrant populations.
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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 | 0.028 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".