Evaluation of Methods and Costs Associated with Recruiting Healthy Women Volunteers to a Study of Ovulation
Bibliographic record
Abstract
OBJECTIVE: We address the crucial and challenging task of anticipating the resources needed to recruit eligible participants for research. We provide our analysis of various recruitment strategies and their cost-effectiveness in our experience in enrolling 610 women for an observational study on ovulation. METHODS: We assess the cost-effectiveness and success of multiple recruitment strategies we employed and provide the estimated cost of labor and materials for each. At enrollment, all participants were asked an open-ended question about how they learned about the study. No financial compensation was provided, but participants received personal hormonal analysis results on completion. RESULTS: Of the 610 enrolled women, 552 provided information on how they learned about the study. The total cost of recruitment was $7645.11, which includes 183 staff hours. The average recruitment cost per participant was $12.53 (ranging from $0 to $118.63). The two methods with the lowest total costs resulted in enrollment of 48% of the recruitment goal using only 0.3% of the budget. In contrast, the two methods with the highest total costs produced 13% of the participants needed but consumed over 72% of the budget. CONCLUSIONS: Low-cost methods are a viable, practical source for attracting healthy women for observational research. Investigators are encouraged to track sources of recruitment and analyze their data at regular intervals during the recruitment phase. Sharing comprehensive recruitment data will assist other researchers to better estimate the resources needed to meet their enrollment goal, leading to more efficient use of time and funding.
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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 | 0.121 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".