Inequities in Care During Pregnancy Loss: Empirical Insights From Experiences With Canadian Perinatal Care
Bibliographic record
Abstract
BACKGROUND: Individuals experiencing perinatal loss are entitled to respectful maternity care, but a paucity of research examines respectful care at the time of pregnancy loss. METHOD: We used data from an online cross-sectional survey (July 2020-February 2022), where 172 individuals reported on early (miscarriage) and late (late second trimester, stillbirth, neonatal death) losses since 2009. We aimed to explore inequities in respectful care experiences among individuals experiencing a late versus early perinatal loss in Canada. We assessed their experiences using the Mothers' Autonomy in Decision Making (MADM) scale and the Mothers on Respect Index (MORi). We created the Compassionate Disclosure of (perinatal) Loss (CDL) index to measure respectful care at the time of a loss. A single separate item, provider not listening to the individual's expression of concerns during pregnancy, was also analyzed. RESULTS: The early and late loss groups differed in education levels. Individuals who self-identified as Indigenous/Black/People of Color (IBPOC) had lower odds of scoring in the top quartile on MADM and MORi scales (AOR = 0.31, 95% CI 0.13, 0.75; AOR = 0.34, 95% CI 0.13, 0.86); and higher odds of reporting that providers did not listen to their concerns prior to the loss (AOR = 2.61, 95% CI 1.24, 5.48). Psychometric analysis supported the CDL index. Participants experiencing late loss had higher odds of reporting top quartile CDL scores than those experiencing early loss (AOR = 3.08, CI 1.22, 7.77). CONCLUSION: Canadian individuals with perinatal loss report disproportionately poorer care when they are experiencing a miscarriage and when they identify as IBPOC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| 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".