Lessons Learned from the Recruitment Process of a South Asian Immigrant Women's Study: Analysis of Research Field Notes
Bibliographic record
Abstract
Menopause, a normal transition in a woman’s life, is an individualized experience with varied attitudes and perceptions. In recent studies, menopausal symptoms and transitional experiences have been found to be greatly influenced by ethnic and cultural differences, as well as immigration. However, visible minority communities, like the South Asian immigrant population in Canada, remain underrepresented in women’s health research literature. The 1.9 million South Asians in Canada comprise 5.6% of the country’s total population and 25.1% of the visible minority population. As a result, there is great interest in improving the process of study recruitment for South Asian immigrant populations. In a menopausal transition study among South Asian immigrant women in the Greater Toronto Area, recruitment strategies were appraised in order to identify how to effectively and efficiently recruit ethnic minority groups for future women’s health studies. The study included all self-reported South Asian women who were: (a) A Canadian citizen or permanent resident; (b) Between the ages of 45 to 55; (c) Born outside of Canada; and (d) Living in Canada for no more than 20 years. A research assistant of South Asian descent recruited the participants using two methods: traditional approach versus technology-based approach. The technology-based approach recruited participants through various relevant Facebook groups. 77 women were approached for participation through their expressed interest on study advertisement postings. Of the 77 women, 38 were eligible (49.4%) and 28 consented and were recruited (73.7%). It was found that the most common reason for ineligibility in the technology-based group was that participants did not meet the minimum age requirement (n=17, 57%). This finding may suggest that South Asian women younger than 45 years old are using Facebook platforms more frequently than women older than 55 years old. Therefore, it may be more effective for recruiters to use a technology-based approach when studying South Asian women populations younger than 45 years of age. Alternatively, in the traditional method, 123 individuals were approached through word-of-mouth. Among the 123 women approached, 33 were eligible (26.8%) and 18 consented and were recruited (54.5%). The eligibility rate, recruitment rate, and recruitment screened ratio were higher for the technology-based approach than the traditional approach. This finding suggests that in addition to the traditional method, a technology-based approach may also be an effective and efficient approach for recruiting participants from the South Asian community.
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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 | 0.221 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".