Short Communication: Previous semen donors and their views regarding the sharing of information with offspring
Bibliographic record
Abstract
BACKGROUND: The UK government has decided to introduce, from 2005, rules that will allow donor-conceived persons to have access to identifying information concerning their donor. This has led to many concerns regarding future gamete donor recruitment. METHODS: Semen donors who had been recruited between 1988 and 2002 were invited to take part in a telephone interview. The interview sought these previous donors' views on issues associated with recruitment, attitudes regarding information sharing and views concerning the offspring. Responses regarding information sharing were compared with their views recorded at the time of recruitment. RESULTS: All 32 donors were recruited altruistically. Eighteen (56%) held the same views concerning the provision of identifying information as they did at the time of recruitment. Of those who had changed their views, eight (25%) expressed a willingness to be more open and four (12%) now wished to be anonymous having previously been unsure. Half of the donors would still have donated if they had been required to be identified to offspring, one-quarter would not have and one-quarter were undecided, although the majority of these said they may have donated under an open system. CONCLUSION: The study shows that it is possible to recruit identifiable donors at this clinic and this suggests that it may be possible for other clinics to do likewise.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".