Mothers’ Opinion About the Quality of New-Born Health Services: Implementation Research in Nampula, Mozambique
Bibliographic record
Abstract
Background: New-born mortality is high in Africa, including in Mozambique (67.3 deaths of children less than one year of age per 1000 live births, 2017).One important factor to reduce this public health burden is ensuring the frequency and quality of newborn visits, with the availability of effectively and timely patient centred care.To reduce the new-born mortality rate in Natikiri, Nampula, teams of researchers from Lúrio University, Mozambique and the University of Saskatchewan, Canada, carried out implementation research, Alert Community for a Prepared Hospital care continuum, which included training programs for health professionals in maternal and child health care as a central component.We planned a mid-project evaluation, to assess the impact of these trainings on the quality of new-born care services at Marrere Health Centre.Methods: This was a quantitative study, applying two cross-sectional surveys about new-born visits quality at the Marrere Health Centre in Natikiri district, on the outskirts of Nampula city in Nampula province, northern Mozambique.The first survey was conducted after two health professional training sessions and the other after five more sessions.The samples of carers of infants up to 28 days of age were surveyed at the Healthy Child Service, Child at Risk Service and Emergency Room, and were calculated considering the average number of post-partum visits per month, 47 in 2018, using a margin of error of 10% and a confidence interval of 90%, and 134 in 2019, using a margin of error of 5% and a confidence interval of 95%.The surveys included a wide variety of user opinion measures of quality and used a five-point Likert scale; the responses were coded and entered REDCap digital database, and analysed to assess frequencies, percentages, mean and standard deviations.This research was approved by the bioethics committees at both Lúrio University and at the University of Saskatchewan.Results: 188 mothers were surveyed at Marrere Health Centre, about the quality of new-born services they had just received.Most areas of childcare services showed no improvement with the trainings.Positive improvements were a 48% increase in health professionals encouraging mothers to share any difficulties during the visit, and a 31% increase in encouraging mothers to have a person of their choice to accompany them during labour, almost always suggesting a traditional birth attendant (97%).
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".