Using online platforms to offer or seek sperm donation: A systematised narrative review of donor and recipient experiences
Bibliographic record
Abstract
Online sperm donation (OSD) is reshaping donor conception by enabling direct connections between donors and recipients outside clinical settings. This systematised narrative review triangulates existing research on sperm donors and recipients who connect through OSD platforms, comparing their perceptions and experiences across different stages of the donation process. By exploring the complex interplay between donors' and recipients' motivations, it fosters dialogue between research areas often examined in isolation. Studies show that donors and recipients, who often explore alternatives alongside OSD, turn to this option to establish contact, gain flexibility, and sometimes facilitate a relationship between the donor and the child. Donors are primarily motivated by altruism or the desire to transmit their genetic heritage, while recipients often turn to OSD for its affordability and flexibility. Both parties engage in negotiations regarding expectations, insemination methods, and future contact, with both parties assessing trust and compatibility. While many report positive experiences, some-especially recipients-face challenges such as communication difficulties, unsolicited advances or sexual misconduct. In the absence of regulation, recipients bear the burden of managing risks related to safety and donor reliability, whereas donors are mainly concerned with legal parentage. Donors and recipients also differ on the donor's role in the child's life, ranging from no involvement to ongoing contact, with post-conception agreements sometimes evolving. Further research is needed to address key gaps, particularly long-term post-conception experiences and perspectives of recipients and online-donor-conceived individuals. This review highlights that rather than eliminating OSD, policies should prioritise safety, transparency, and informed decision-making while expanding access to fertility clinics and establishing a clear legal framework. A balanced approach-one that respects autonomy while mitigating risks-will be essential to fostering ethical and sustainable donor-recipient arrangements, ensuring positive outcomes, especially for recipients and their families.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".