Membrane fouling and its control in drinking water membrane filtration process
Bibliographic record
Abstract
Recruitment in research can be challenging, particularly for racial/ethnic minorities and immigrants. There remains a dearth of research identifying the health and sociocultural needs of these populations related to recruitment. To describe our experiences and lessons learned in recruiting African immigrant (AI) women for the AfroPap study, a community-based study examining correlates of cervical cancer screening behaviors. We developed several recruitment strategies in collaboration with key informants and considered published recruitment methods proven effective in immigrant populations. We also evaluated the various recruitment strategies using recruitment records and study team meeting logs. We enrolled 167 AI women in the AfroPap study. We used the following recruitment strategies: (1) mobilizing African churches; (2) utilizing word of mouth through family and friends; (3) maximizing research team's cultural competence and gender concordance; (4) promoting altruism through health education; (5) ensuring confidentiality through the consenting and data collection processes; and (6) providing options for data collection. Online recruitment via WhatsApp was an effective recruitment strategy because it built on existing information sharing norms within the community. Fear of confidentiality breaches and time constraints were the most common barriers to recruitment. We were successful in recruiting a "hard-to-reach" immigrant population in a study to understand the correlates of cervical cancer screening behaviors among AI women by using a variety of recruitment strategies. For future research involving African immigrants, using the internet and social media to recruit participants is a promising strategy to consider.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".