Perspectives of Canadian Oocyte Donors and \nRecipients on Donor Compensation and the \nEstablishment of a Personal Health Information \nRegistry
Bibliographic record
Abstract
We report the views of 33 women who were involved in an altruistic \noocyte donation program about provisions under Canada’s Assisted \nHuman Reproduction Act 2004 to prohibit donor compensation and \nto establish a Personal Health Information Registry. The participants \nhad been either donors of oocytes to a recipient known to them (15) \nor recipients of such donation (18) through services provided by a \nclinic in a large Canadian city, and they each participated in a semistructured \nface-to-face or telephone interview. Among the 15 donor \nparticipants, seven were friends of the recipient, six were sisters, one \nwas a niece of the recipient, and one donor donated twice, once to \nher sister and once to a friend. In eight cases the donor and recipient \nparticipated in interviews independently. At the time of interview, 11 \nof the 25 separate cases had resulted in a live birth and one in an \nongoing pregnancy, so that “successful” and “unsuccessful” donations \nwere equally represented among participants. While divergent \nviews were reported among and between donors and recipients on \nan altruistic model versus a compensated model of donation, most \nparticipants largely endorsed the establishment of a personal health \ninformation registry.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".