Effect of a two-component intervention to change hospital practice from early to delayed umbilical cord clamping in the Peruvian Amazon.
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of a two-component intervention to change hospital practice with regard to the timing of umbilical cord clamping. METHODS: A pre-/post-study design was used to measure the effect of a two-component intervention on mean time to clamp the umbilical cord. The study took place at Hospital Iquitos "César Garayar García" in Iquitos, Peru. A total of 224 women were recruited from the hospital labor room: 112 pre-intervention, from 18 May-3 June 2009, and 112 post-intervention, from 6-20 July 2009. The intervention consisted of 1) a "best practice" three-day training workshop on birthing, and 2) a hospital directive. All deliveries were observed and the time between delivery of the first shoulder and clamping of the umbilical cord was measured with a digital stopwatch. RESULTS: The mean time between delivery and cord clamping before the intervention was 56.8 seconds (95% confidence interval [CI]: 51.0, 62.7). This increased to 169.8 seconds (95% CI: 153.8, 185.8) following the intervention. The difference in mean time to clamp remained significant in multivariate analyses (β adjusted = 113.2 seconds, 95% CI: 96.6, 129.9). CONCLUSIONS: Hospital policy and practice can be successfully changed from early to delayed umbilical cord clamping using a simple, two-component intervention.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".