Is care of stillborn babies and their parents respectful? Results from an international online survey
Bibliographic record
Abstract
OBJECTIVE: To quantify parents' experiences of respectful care around stillbirth globally. DESIGN: Multi-country, online, cross-sectional survey. SETTING AND POPULATION: Self-identified bereaved parents (n = 3769) of stillborn babies from 44 high- and middle-income countries. METHODS: Parents' perspectives of seven aspects of care quality, factors associated with respectful care and seven bereavement care practices were compared across geographical regions using descriptive statistics. Respectful care was compared between country-income groups using multivariable logistic regression. MAIN OUTCOME MEASURES: Self-reported experience of care around the time of stillbirth. RESULTS: A quarter (25.4%) of 3769 respondents reported disrespectful care after stillbirth and 23.5% reported disrespectful care of their baby. Gestation less than 30 weeks and primiparity were associated with disrespect. Reported respectful care was lower in middle-income countries than in high-income countries (adjusted odds ratio 0.35, 95% CI 0.29-0.42, p < 0.01). In many countries, aspects of care quality need improvement, such as ensuring families have enough time with providers. Participating respondents from Latin America and southern Europe reported lower satisfaction across all aspects of care quality compared with northern Europe. Unmet need for memory-making activities in middle-income countries was high. CONCLUSIONS: Many parents experience disrespectful care around stillbirth. Provider training and system-level support to address practical barriers are urgently needed. However, some practices (which are important to parents) can be readily implemented such as memory-making activities and referring to the baby by name. TWEETABLE ABSTRACT: One in four experience disrespectful care after stillbirth. Parents want more time with providers and their babies, to talk and memory-make.
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".