‘ <i>I am happy to be listened to’</i> : co-creation of a simple tool to measure women’s experiences of respectful maternity care in urban Tanzania
Bibliographic record
Abstract
BACKGROUND: Rights-based Respectful Maternity Care (RMC) is crucial for quality of care and improved birth outcomes, yet RMC measurements are rarely included in facility improvement initiatives. We aimed to (i) co-create a routine RMC measurement tool (RMC-T) for congested maternity units in Dar es Salaam, Tanzania, and (ii) assess the RMC-T's acceptability among women and healthcare stakeholders. METHOD: We employed a participatory approach utilizing multiple mixed methods. This included a scoping review, stakeholder engagement involving postnatal women, healthcare providers, health leadership, and global researchers through interviews, focus groups, and two surveys involving 201 and 838 postnatal women. Cronbach's alpha and factor analysis were conducted for validation using Stata 15. Theories of social practice and Thematic Framework of Acceptability guided the assessment of stakeholder priorities and tool acceptability. RESULTS: The multi-phased iterative co-creation process produced the 25-question RMC-T that measures satisfaction, communication, mistreatment (including physical, verbal, and sexual abuse; neglect; discrimination; lack of privacy; unconsented care; post-birth clean-up; informal payments; and denial of care), supportive care (such as food intake and mobility), birth companionship, post-procedure pain relief, bed-sharing, and newborn respect. The pragmatic validation process prioritized stakeholder feedback over strict statistics, lowering Cronbach's alpha from 0.70 in version 1 to 0.57 for the RMC-T. Women valued the opportunity to share their experiences. CONCLUSIONS: The RMC-T is contextualized, validated, and acceptable for measuring women's experiences of RMC. Routine use in facility-based quality improvement initiatives, along with targeted actions to address gaps, will advance rights-based RMC. Further validation and community-based studies are needed.
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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 | 0.049 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".