Weight-based disparities in perinatal care: quantitative findings of respect, autonomy, mistreatment, and body mass index in a national Canadian survey
Bibliographic record
Abstract
BACKGROUND: Qualitative studies document episodes of weight-related disrespectful care, particularly for people with high body mass index (BMI ≥ 30) and reveal implicit and explicit biases in health care providers. No large quantitative studies document the pervasiveness of weight stigma or if experiences change with increasing BMI. METHODS: The multi-stakeholder RESPCCT study team designed and distributed a cross-sectional survey on the experiences of perinatal services in all provinces and territories in Canada. From July 2020 to August 2021, participants who had a pregnancy within ten years responded to closed and open-ended questions. Chi square analysis assessed differences in mean scores derived from three patient-reported experience measures of autonomy (MADM), respect (MOR), and mistreatment (MIST). Controlling for socio-demographic factors, multivariate logistic regression analysis explored relationships between different BMI categories and respectful care. RESULTS: Of 4,815 Canadians who participated, 3,280 with a BMI of ≥ 18.5 completed all the questions. Pre-pregnancy BMI was significantly associated with race/ethnicity, income sufficiency, and education but not with age. Individuals with higher BMIs were more likely to experience income insufficiency, have lower levels of education, and more frequently self-identified as Indigenous or White. Those with BMI ≥ 35 exhibited notably higher odds of reduced autonomy (MADM) scores, with an unadjusted odds ratio of 1.62 and an adjusted odds ratio of 1.45 compared to individuals with a normal weight. Individuals with BMIs of 25-25.9, 30-34.9, and ≥ 35 exhibited odds of falling into the lower tercile of respect (MOR) scores of 1.34, 1.51, and 2.04, respectively (p < .01). The odds of reporting higher rates of mistreatment (top 33% MIST scores) increased as BMI increased. CONCLUSIONS: While socio-demographic factors like race and income play significant roles in influencing perinatal care experiences, BMI remains a critical determinant even after accounting for these variables. This study reveals pronounced disparities in the provision of respectful perinatal care to pregnant individuals with higher BMIs in Canada. Data suggest that those with higher BMIs face disrespect, discrimination, and mistreatment. Identification of implicit and explicit weight bias may give providers insight enabling them to provide more respectful care.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".