In search of stool donors: a multicenter study of prior knowledge, perceptions, motivators, and deterrents among potential donors for fecal microbiota transplantation
Bibliographic record
Abstract
Fecal microbiota transplantation (FMT) is a highly effective therapy for recurrent Clostridioides difficile infection. Stool donors are essential, but difficult to recruit and retain. We aimed to identify factors influencing willingness to donate stool. This multi-center study with a 32-item questionnaire targeted young adults and health care workers via social media and university email lists in Edmonton and Kingston, Canada; London and Nottingham, England; and Indianapolis and Boston, USA. Items included baseline demographics and FMT knowledge and perception. Investigated motivators and deterrents included economic compensation, screening process, time commitment, and stool donation logistics. Logistic regression and linear regression models estimated associations of study variables with self-assessed willingness to donate stool. 802 respondents completed our questionnaire: 387 (48.3%) age 21-30 years, 573 (71.4%) female, 323 (40%) health care workers. Country of residence, age and occupation were not associated with willingness to donate stool. Factors increasing willingness to donate were: already a blood donor (OR 1.64), male, altruism, economic benefit, knowledge of how FMT can help patients (OR 1.32), and positive attitudes towards FMT (OR 1.39). Factors decreasing willingness to donate were: stool collection unpleasant (OR 0.92), screening process invasive (OR 0.92), higher stool donation frequency, negative social perception of stool, and logistics of collection/transporting feces. We conclude that 1) blood donors and males are more willing to consider stool donation; 2) altruism, economic compensation, and positive feedback are motivators; and 3) screening process, high donation frequency, logistics of collection/transporting feces, lack of public awareness, and negative social perception are deterrents. Considering these variables could maximize donor recruitment and retention.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".