Nurse Mentor Training Program to Improve Quality of Maternal and Newborn Care at Primary Health Centres: Process Evaluation
Bibliographic record
Abstract
Quality of maternal and newborn care could be improved if health care providers’ knowledge and competencies as well as system level constraints are addressed. However, due to several barriers staff nurses who form the frontline of health care workforce have limited access to enhancing their clinical knowledge and competencies. To address this gap, a new cadre of nurse mentors (NMs) for the public health system were trained by specialists from a teaching hospital in a special 5-week training course. This included 54 hours of theory and 110 hours of practical in clinical obstetric and newborn care, apart from mentoring, quality improvement and health systems issues. The nurse mentors were assigned to support staff nurses in the primary health care centres (PHCs) in eight northern Karnataka districts. Each NM covered 6-8 PHCs monthly for 2 - 3 days and thus a total of 385 PHCs were reached. They received support in the field through supportive supervision visits done by the specialists who had trained them, as well as by refresher training and clinical postings to the district hospitals. This paper presents impact of the training program on change in immediate and long term knowledge and competency scores of nurse mentors. Their baseline knowledge scores changed from 44.3 ± 12.7 to 72.1 ± 13.8 immediately after the training in obstetric and from 18.2 ± 19.1 to 66.4 ± 14.9 in newborn (p p p > 0.05). Skills score soon after training increased from 62.2 ± 13.2 to 69.6 ± 12.5 in obstetric after a 1 year period and from 52.6 ± 9.3; 63.5 ± 14.4 in newborn (p < 0.001) content areas respectively. These findings have implications for those interested in improving quality of maternal and child care through nurse-dependent health delivery systems.
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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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".