Measuring disrespect and abuse during childbirth in a high-resource country: Development and validation of a German self-report tool
Bibliographic record
Abstract
INTRODUCTION: Increasing evidence on disrespect and abuse during childbirth has led to growing concern about the quality of care childbearing women are experiencing. To provide quantitative evidence of disrespect and abuse during childbirth services in Germany a validated measurement tool is needed. RESEARCH AIM: The aim of this research project was the development and psychometric validation of a survey tool in the German language that measures disrespect and abuse of women during childbirth. METHODS: A survey tool was created including the following measures: German adaptations of the short and long form of the "Mothers on Respect" (MOR) index (MOR-7 and MOR-G); the "Mothers' Autonomy in Decision Making" (MADM) scale; a mistreatment-index (MIST-I) comprising indicators of mistreatment during childbirth; and a set of items that measure experiences of discrimination during maternity care. Internal consistency reliability and construct validity of the scales were assessed using Cronbach's alpha, unweighted least squares factor analysis and non-parametric correlation analysis with a scale that measures a related construct, the Posttraumatic Symptom Scale - Self Report (PSS-SR) scale. We distributed the survey online, recruiting through snowball sampling via social media. A selection bias towards women who had experienced disrespect and abuse during their birth was intended and expedient for tool validation. The final sample of participants (n = 2045) had given birth in Germany between 2009 and 2018. FINDINGS: More than 77% of the study participants reported at least one form of mistreatment with non-consented care being the most commonly reported type of mistreatment, followed by physical violence, violation of physical privacy, verbal abuse and neglect. All included scales showed good psychometric properties with high Cronbach's alphas (0.95 for both MOR versions and 0.96 for MADM). Factor analysis generated one factor scales with high factor loadings (0.75 to 0.92 for MOR-7; 0.37 to 0.90 for MOR-G and 0.83 to 0.92 for MADM). MOR-7, MOR-G, MADM and MIST-I scores were significantly (p<0.001) correlated with PSS-SR scores (Spearman's rho -0.70, -0.61 and 0.68 for MOR-G, MADM and the MIST-I, respectively). CONCLUSIONS: This study presents a valid and reliable instrument for the quantitative assessment of disrespect and abuse during childbirth in Germany. Childbearing women's experiences of disrespect and abuse are a relevant phenomenon in German hospital based maternity care. Disrespect and abuse during childbirth appear to contribute to post-traumatic symptoms and may be associated with severe mental health problems postpartum.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".