Improving the knowledge of labour and delivery nurses in India: a randomized controlled trial of mentoring and case sheets in primary care centres
Bibliographic record
Abstract
BACKGROUND: Birthing in health facilities in India has increased over the last few years, yet maternal and neonatal mortality rates remain high. Clinical mentoring with case sheets or checklists for nurses is viewed as essential for on-going knowledge transfer, particularly where basic training is inadequate. This paper summarizes a study of the effect of such a programme on staff knowledge and skills in a randomized trial of 295 nurses working in 108 Primary Health Centres (PHCs) in Karnataka, India. METHODS: Stratifying by district, half of the PHCs were randomly assigned to be intervention sites and provided with regular mentoring visits where case sheet/checklists were a central job and teaching aid, and half to be control sites, where no support was provided except provision of case sheets. Nurses' knowledge and skills around normal labour, labour complications and neonate issues were tested before the intervention began and again one year later. Univariate and multivariate analyses were conducted to examine the effect of mentoring and case sheets. RESULTS: Overall, on none of the 3 measures, did case sheet use without mentoring add anything to the basic nursing training when controlling for other factors. Only individuals who used both case-sheets and received mentoring scored significantly higher on the normal labour and neonate indices, scoring almost twice as high as those who only used case-sheets. This group was also associated with significantly higher scores on the complications of labour index, with their scores 2.3 times higher on average than the case sheet only control group. Individuals from facilities with 21 or more deliveries in a month tended to fare worse on all 3 indices. There were no differences in outcomes according to district or years of experience. CONCLUSIONS: This study demonstrates that provision of case sheets or checklists alone is insufficient to improve knowledge and practices. However, on-site mentoring in combination with case sheets can have a demonstrable effect on improving nurse knowledge and skills around essential obstetric and neonatal care in remote rural areas of India. We recommend scaling up of this mentoring model in order to improve staff knowledge and skills and reduce maternal and neonatal mortality in India. TRIAL REGISTRATION: This study is registered at clinicaltrials.gov, Identifier No. NCT02004912 , November 27, 2013.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".