Stillbirth, still life: A qualitative patient-led study on parents’ unsilenced stories of stillbirth
Bibliographic record
Abstract
Objective: Explore parents’ experiences of stillbirth using a patient-led qualitative approach. Methods: Parents who had experienced stillbirth in the previous 5 years were recruited through posters and snowball sampling, each participating in one or more data collection event. We conducted a co-design focus group to set the direction of our research, narrative interviews, and a reflect focus group to engage parents in finalizing the analysis and findings. Data were analysed iteratively using a participatory grounded theory approach. Results: Parents’ (n=11) experiences tended to be expressed in the form of two narratives: clinical and personal; the historical silent discourse permeated both narratives. The clinical experience, Abandoned in silence, was sub-divided into three categories: 1) Lead me through the decision with one sub-category: Recognize that I am having a birth and death experience; 2) I need specialized care now; and 3) I need specialized care later. The personal experience, Shrouded in silence, was sub-divided into three categories: 1) I survived the space between; 2) I am learning to forge a new path; and 3) My daughter’s name is Charlotte. Stillbirth is a story of death, but it is also a story of life. In stillbirth, parents require the space to experience both the birth and death elements of the story; yet, one or both elements are often silenced. Stillbirth, still Life was the core concept that emerged from parents’ stories of their stillborn babies. Conclusion: Parents’ narratives are driven by the need to honour their babies’ lives. They are learning to be unsilenced.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".