Promoting respectful maternity care in rural Tanzania: nurses’ experiences of the “Health Workers for Change” program
Bibliographic record
Abstract
BACKGROUND: Disrespectful and abusive care of women during their pregnancies has been shown to be a barrier for women accessing health care services for antenatal care and delivery. As part of an implementation research study to improve women's access to health care services in Rorya District, Mara, Tanzania, we conducted a pilot study training reproductive health care nurses to be more sensitive to women's needs based on the "Health Workers for Change" curriculum. METHODS: Six series of workshops were held with a total of 60 reproductive health care nurses working at the hospitals, health centres and dispensaries in the district. The participants provided comments on a survey and participated in focus groups at the conclusion of the workshop series. These qualitative data were analyzed for common themes. RESULTS: The participants appreciated the training and reflected on the poor quality of health care services they were providing, recognizing their attitudes towards their women patients were problematic. They emphasized the need for future training to include more staff and to sustain positive changes. Finally, they made several suggestions for improving women's experiences in the future. CONCLUSIONS: The qualitative findings demonstrate the success of the workshops in assisting the health care providers to become aware of their negative attitudes towards women. Future research should examine the impact of the workshops both on sustaining attitudinal changes of the providers and on the experiences of pregnant women receiving health care services.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".