Open Adoption: Adoptive Parents' Experiences of Birth Family Contact and Talking to Their Child about Adoption
Bibliographic record
Abstract
The trend towards more open adoption presents adopters with unique parenting challenges associated with satisfying the child's curiosity about their origins and maintaining relationships with birth family through contact. This article by Mandi MacDonald and Dominic McSherry focuses on the experiences of 20 sets of adoptive parents who were interviewed as part of the Northern Ireland Care Pathways and Outcomes Study. Interviews were analysed following the principles of Interpretative Phenomenological Analysis (IPA). The article explores adoptive parents' experience of talking to their child about adoption and of post-adoption contact with members of the birth family. Adopters discussed adoption sensitively with their child but were concerned that difficult and complex family histories would present a risk to the child's self-esteem and emotional well-being. All forms of contact proved emotionally and practically burdensome; however, adopters were committed to making it work for the child's benefit and were open to increased contact should the child wish it in the future. There was little relationship with birth family outside of formal contact. The study reveals the need for a mechanism to facilitate communication with birth families if adopters are to be able to respond to the child's changing need for contact and information.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".