The experience of spontaneous pregnancy loss for infertile women who have conceived through assisted reproduction technology
Bibliographic record
Abstract
BACKGROUND: The purpose of this qualitative, phenomenological study was to explore the subjective experiences of infertile women who conceived through the use of assisted reproduction technology--ovarian stimulation, intrauterine insemination or IVF--only to lose their pregnancy at 2-16 weeks gestation. METHODS: Ten women participated in in-depth, tape-recorded interviews. After initial content analysis, a phenomenological analysis was undertaken to identify common themes in the participants' stories. RESULTS: Nine common themes were identified. These included: a sense of profound loss and grief; diminished control; a sense of shared loss with their partners; injustice or lack of fairness; ongoing reminders of the loss; social awkwardness; fear of re-investing in the treatment process or a subsequent pregnancy; the need to make sense of their experience; and feelings of personal responsibility for what had happened. CONCLUSIONS: Participants' experiences of pregnancy loss were embedded within their experiences of infertility and medical treatment, and shaped by their significant investment in having a child. A significant feature was their marked ambivalence regarding future reproductive options after their pregnancy loss, reflecting a unique overlay of prominent anxiety in their grief experience.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".