From clicks to creating kin: how Australian online egg donors craft relationships with recipients and donor-conceived children
Bibliographic record
Abstract
Anonymous egg donation is prohibited in Australia, with all states allowing donor-conceived people (DCPs) to access their donor's identity at age 18 or 16, depending on the state. However, early contact, well before age 18, is becoming more common. A key driver of this trend is recipients' and donors' use of online platforms (OPs) like Facebook to find one another, enabling donor-recipient contact before donation and/or after the donor-conceived children are born. This study reports on interviews with 24 egg donors who met recipients via OPs and had early contact post-birth. Using reflexive thematic analysis, the study found that donors were primarily motivated by empathy and saw donation as a relational act. They selected recipients with shared values around early contact and negotiated post-birth relationships. Early contact often led to meaningful kinship connections, with relationships described using extended family terms. The donor-recipient relationship unfolded as a progressive relational model: motivations informed recipient choice and contact expectations, and early contact deepened relational bonds. However, some donors experienced relationship breakdowns with recipients, illustrating the emotional complexity of (early) contact, even when agreed to. Findings underscore the importance of psychosocial support to ensure donor conception practices promote the wellbeing of all parties involved.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".