WHAT MOTIVATES WOMEN TO TAKE PART IN CLINICAL AND BASIC SCIENCE ENDOMETRIOSIS RESEARCH?*
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to identify factors motivating women to take part in endometriosis research and to determine if these factors differ for women participating in clinical versus basic science studies. METHODS: A consecutive series of 24 women volunteering for participation in endometriosis-related research were asked to indicate, in their own words, why they chose to volunteer. In addition, the women were asked to rate, on a scale of 0 to 10, sixteen potentially motivating factors. The information was gathered in the form of an anonymous self-administered questionnaire. RESULTS: Strong motivating factors (mean score > 8) included potential benefit to other women's health, improvement to one's own condition, and participation in scientific advancement. Weak motivating factors (mean score < 3) included financial compensation, making one's doctor happy, and use of 'natural' products. No difference was detected between clinical and basic science study participants. CONCLUSION: This study is the first study to specifically investigate the factors that motivate women to take part in endometriosis research. Understanding why women choose to take part in such research is important to the integrity of the informed consent process. The factors most strongly motivating women to participate in endometriosis research related to improving personal or public health; the weakest, to financial compensation and pleasing the doctor.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".