Recruitment of Community-Based Samples: Experiences and Recommendations for Optimizing Success
Bibliographic record
Abstract
BACKGROUND: Recruitment in health and social science research is a critically important but often overlooked step in conducting successful research. The challenges associated with recruitment pertain to multiple factors such as enrolling groups with vulnerabilities, obtaining geographic, cultural, and ethnic representation within study samples, supporting the participation of less accessible populations such as older adults, and developing networks to support recruitment. PURPOSE: This paper presents the experiences of two early career researchers in recruiting community-based samples of older adults, their caregivers, and associated health providers. METHODS: Challenges and facilitators in recruiting two community-based qualitative research samples are identified and discussed in relation to the literature. RESULTS: Challenges included: identifying potential participants, engaging referral partners, implementing multi-methods, and achieving study sample diversity. Facilitators included: making connections in the community, building relationships, and drawing on existing networks. CONCLUSIONS: Findings suggest the need for greater recognition of the importance of having clear frameworks and strategies to address recruitment prior to study commencement as well as the need to have clear outreach strategies to optimize inclusion of marginalized groups. Recommendations and a guide are provided to inform the development of recruitment approaches of early career researchers in health and social science research.
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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 | 0.216 | 0.229 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier 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".