Strategies discussed at the XIIth international conference on Kangaroo mother care for implementation on a countrywide scale
Bibliographic record
Abstract
AIM: Building strategies for the country-level dissemination of Kangaroo mother care (KMC) to reduce the mortality rate in preterm and low birth weight babies and improve quality of life. KMC is an evidence-based healthcare method for these infants. However, KMC implementation at the global level remains low. METHODS: The international network in Kangaroo mother brought 172 KMC professionals from 33 countries together for a 2-day workshop held in conjunction with the XIIth International KMC Conference in Bogota, Colombia, in November 2018. Participants worked in clusters to formulate strategies for country-level dissemination and scale-up according to seven pre-established objectives. RESULTS: The minimum set of indicators for KMC scale-up proposed by the internationally diverse groups is presented. The strategies for KMC integration and implementation at the country level, as well as the approaches for convincing healthcare providers of the safety of KMC transportation, are also described. Finally, the main aspects concerning KMC follow-up and KMC for term infants are presented. CONCLUSION: In this collaborative meeting, participants from low-, middle- and high-income countries combined their knowledge and experience to identify the best strategies to implement KMC at a countrywide scale.
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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 | 0.038 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".