Why are there delays in seeking treatment for childhood diarrhoea in <scp>I</scp>ndia?
Bibliographic record
Abstract
AIM: To examine the barriers and facilitating factors for seeking treatment for childhood diarrhoea and to determine the main causes for delay in seeking treatment. METHODS: Data from Indian Demographic and Health survey 2005-06 (NFHS-III) were used. Mothers were asked whether their children (<5-years) had suffered from diarrhoea during the 2 weeks preceding the survey. Data were collected on the time of seeking treatment after start of the illness and days waited to seek treatment after the diarrhoea started. Multivariate logistic regression analysis was performed to find the determinants of seeking treatment at the health facility and the factors responsible for the 'delay' in seeking advice/treatment. RESULTS: Of a sample of 41 287 children, 3890 (9.4%) reportedly had diarrhoea. Sixty percentage of children with diarrhoea were taken to a health facility. Mother's education till higher secondary and above (OR 1.65; 95% CI, 1.08-2.54), richest (OR 1.76; 95% CI, 1.24-2.48) wealth index, and possession of a health card by the mother (OR 1.35; 95% CI, 1.12-1.62) increased the odds of seeking treatment. There was a strong gender bias; a male child had lower odds of experiencing a 'delay' in seeking treatment, compared with a female child (OR 0.71; 95% CI, 0.55-0.92). Access to a health facility still remains a major issue: treatment seeking was delayed when distance to a health facility was reported as a 'major problem' (OR 1.33; 95% CI, 1.01-1.76). CONCLUSION(S): Improved care seeking for childhood diarrhoea in India is still constrained by access to a health facility and requires expansion and strengthening of the public health system. The caregivers, especially the mothers need to be educated about the importance of seeking timely treatment and the benefits of oral rehydration solution.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".