First experiences of accrediting district hospitals for excellence in newborn care in KwaZulu-Natal, South Africa: Successes and challenges
Bibliographic record
Abstract
Objective: Providing quality health care is what all health facilities seek to achieve. Accreditation of health services are used to assess and improve the quality of health care in different settings. This study describes experiences of developing and conducting accreditation for excellence in newborn care in district hospitals in KwaZulu-Natal (KZN), South Africa following a 3-year programme of support to all nurseries in KZN.Methods: A facility review was conducted in district hospitals in KZN to evaluate the quality of care provided to newborn babies to accredit hospitals in newborn care. Multiple tools were used to assess different components of care from different perspectives, including record reviews, assessment of staff skills and interviews with mothers. Awarding accreditation was based on scores achieved in various domains, which contributed to an overall score. Compliance with key priority indicators was required for accreditation to be awarded.Results: Overall scores for accreditation ranged between 57%-93%. Mothers reported high levels of satisfaction with care received. Record reviews identified shortfalls in care provided, and skills assessments showed poor resuscitation skills in labour wards in some hospitals. Of 39 district hospitals, eight were awarded silver and five were awarded gold accreditation status.Conclusions: This accreditation of newborn care provides a workable model for undertaking accreditation in district hospitals and can be used by managers to identify and address shortfalls in care. Regular accreditation would support ongoing quality improvement (QI) in neonatal care and such a process could be applied to other aspects of care in health facilities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".