An Analysis of the Quality of New-Born Health Services in Nampula, Mozambique.
Bibliographic record
Abstract
Abstract Background New-born morbidity and mortality are high in Africa, including Mozambique. One important factor to reduce this public health burden is ensuring the frequency and quality of new-born visits, with the availability of efficient, timely, patient centred care. To contribute to the reduction of new-born mortality rate in Nampula, the Lúrio University and the University of Saskatchewan, carried out an implementation research project which included training activities for health professionals in maternal and child health care. We planned a mid-project evaluation, to assess the impact of health professionals training on the quality of services at Marrere Health Centre. Methods Quantitative study, applying two cross-sectional surveys about new-born visits service quality. The first surveys were conducted after two health professionals’ training sessions and the other after five more sessions. The samples of carers of infants up to 28 days of age in Marrere Health Centre, were calculated considering the average number of post-partum visits per month (47 in 2018, with a margin of error of 10% and a confidence interval of 90 %, 134 in 2019, with a margin of error of 5% and a confidence interval of 95%). The individual surveys used a five-point Likert scale and were entered into REDCap , and analysed to assess frequency, percentage, mean and standard deviation. This research was approved by the bioethics committees at Lúrio University and at the University of Saskatchewan. Results 188 child carers were surveyed at Marrere Health Centre, about new-born services quality. Most areas showed no improvement. Positive improvements were a 48% increase in health professionals encouraging mothers to share any difficulties during the patient encounter, a 31% increase in encouraging mothers to have a person of their choice to accompany them during labour, suggesting a traditional birth attendant (97%). Many shortcomings persisted in practices of introducing themselves, communication with patients, privacy, and confidentiality. Conclusion The quality of care at Marrere Health Centre did not improve and health professionals are not practising according to the protocol. Reviewing health professionals learning approach, developing continuous capacity building, would be the next best steps to improve quality of new-born centred care. Trial registration This study was not registered in any data base.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".