Strengthening Recruitment and Retention: Mitigation Strategies in Two Longitudinal Studies of Pregnant Women in Pakistan
Bibliographic record
Abstract
PURPOSE: Global health researchers have a responsibility to conduct ethical research in a manner that is culturally respectful and safe. The purpose of this work is to describe our experiences with recruitment and retention in Pakistan, a low-middle-income country. DESCRIPTION: We draw on two studies with a combined sample of 2161 low-risk pregnant women who participated in a pilot (n = 300) and a larger (n = 1861) prospective study of psychological distress and preterm birth at one of four centers (Garden, Hyderabad, Kharadar, Karimabad) of the Aga Khan University Hospital in Karachi, Pakistan. ASSESSMENT: Challenges we encountered include economic hardship and access to healthcare; women's position in the family; safety concerns and time commitment; misconceptions and mistrust in the research process; and concerns related to blood draws. To mitigate these challenges, we developed culturally acceptable study incentives, involved family members in the decision-making process about study participation, partnered with participants' obstetrician-gynecologists, accommodated off site study visits, combined research visits with regular prenatal care visits, and modified research participation related to blood draws for some women. CONCLUSION: Implementation of these mitigation strategies improved recruitment and retention success, and we are confident that the solutions presented will support future scientists in addressing sociocultural challenges while embarking on collaborative research projects in Pakistan and other low-middle-income countries.
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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 | 0.081 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".