Educational barriers of nurses caring for sick and at‐risk infants in <scp>I</scp>ndia
Bibliographic record
Abstract
AIM: To gain ideas and information from healthcare providers to optimize the education and clinical practices of nurses caring for sick or at-risk newborns in India. BACKGROUND: Improving infant survival has been identified as a Millennium Development Goals; however, India still faces many challenges with 3.1 million neonatal deaths and 2.6 million stillbirths annually. Skilled nursing care has been associated with decreased morbidity and mortality in newborns. However, core competencies in newborn care education and training are lacking for nurses. METHODS: Qualitative data were collected from 12 focus groups with 101 newborn care providers from three areas of India as well as from a 2-day stakeholders' meeting. Data analysis was undertaken using descriptive and thematic content analysis. RESULTS: Perceived challenges included limited manpower and high nurse turnover, lack of access to evidence-based orientation to newborn care and problems with access to appropriate learner-based, neonatal training. Relevant, ongoing education opportunities, led by nursing leaders were identified to be important solutions. CONCLUSION: Findings provide insight into the current healthcare system in India with specific reference to the nursing care of at-risk newborns. There is a lack of existing resources to provide standardized and specific orientation curricula for nurses. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Policy makers in health and education need to: support and enact learner-based orientation and continuing educational opportunities as well as ongoing competency-based education programmes; encourage nurse leader involvement and support; and provide sustainable system-related supports. Nurses and other health providers need to work together to influence government policy.
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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 | 0.000 | 0.002 |
| 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".