‘<i>Know‐Can</i>’ gap: gap between knowledge and skills related to childhood diarrhoea and pneumonia among frontline workers in rural Uttar Pradesh, India
Bibliographic record
Abstract
OBJECTIVES: In India, frontline workers (FLWs) - public accredited social health activists (ASHAs) and private rural medical providers (RMPs) - are important for early detection and treatment of childhood diarrhoea and pneumonia. This cross-sectional study aims to measure knowledge and skills, and the gap between the two ('know-can' gap), regarding assessment of childhood diarrhoea with dehydration and pneumonia among FLWs, and to explore factors associated with them. METHODS: We surveyed 473 ASHAs and 447 RMPs in six districts of Uttar Pradesh. We assessed knowledge and skills using face-to-face interviews and video vignettes, respectively, about key signs of both conditions. The 'know-can' gap corresponds to absent skills among FLWs with correct knowledge. We used logistic regression to identify the correlates of knowledge and skills. RESULTS: FLWs' correct knowledge ranged from 23% to 48% for dehydration signs and 27% to 37% for pneumonia signs. Their skills ranged from 3% to 42% for dehydration and 3% to 18% for pneumonia. There was a significant 'know-can' gap in all the signs, except 'sunken eyes'. Training and supervisory support was associated with better knowledge and skills for diarrhoea with dehydration, but only better knowledge for pneumonia. CONCLUSIONS: FLWs are crucial to the Indian health system, and high-quality FLW services are necessary for continued progress against under-five deaths. The gap between FLWs' knowledge and skills warrants immediate attention. In particular, our results suggest that knowledge-focused trainings are insufficient for FLWs to convert knowledge into appropriate assessment skills.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".