An Analysis of the Quality of Maternity Services in Nampula, Mozambique.
Bibliographic record
Abstract
Abstract BackgroundMaternity service quality is essential to reduce maternal and new-born morbidity and mortality (extremely high in Africa, including Mozambique). In Mozambique, maternal mortality rate is 451.6 maternal deaths per 100000 live births (2017). The reasons for this are complex, but one important factor to reduce this burden is ensuring the quality of maternity services, with the availability of efficient care, to improve institutional deliveries. To contribute to reduce maternal and new-born mortality rates in Natikiri, Nampula, the Lúrio University and the University of Saskatchewan, carried out an implementation research, including training activities for health professionals in maternal and child health care. We planned a mid-project evaluation, to assess the impact of the trainings, on the quality of services at Marrere Hospital Maternity.MethodsQuantitative pre-post study, applying two cross-sectional surveys about maternity service quality, one of the surveys being conducted after five health professionals’ trainings and the other after six more trainings. The two surveys included samples of post-partum women in the maternity, calculated with a 10% margin error and 90% confidence interval for the first survey, and with a 7% margin error and 95% confidence interval for the second. The surveys were entered into REDCap and analysed to assess frequency, percentage, mean and standard deviation. This research was approved by the Institutional Committees of Bioethics at Lúrio University and at the University of Saskatchewan.Results116 post-partum women were surveyed at the maternity, assessing standards of patient centred care during delivery labour. Most areas showed no improvement. Some positive improvements were: delivering women were given the option to have a person of their choice to accompany them during labour (75%), notably a traditional birth attendant (34%), and they had continuous support from an health professional (68%). But many shortcomings persisted in areas of privacy (33%), and confidentiality (57%). ConclusionThe quality of patient centred care at Marrere General Hospital Maternity, did not improve with health professionals training. Decreasing the large turnover rate, and reviewing health professionals learning styles, promoting continuous professional capacity building, would be the next steps to improve quality of patient centred care.Trial registrationThis study was not registered in any data base.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".