Summary of best evidence on kangaroo mother care for premature infants
Bibliographic record
Abstract
Objective To evaluate and summarize the best available evidence of kangaroo mother care for premature infants both in China and abroad, and to provide evidence-based support for clinical practice. Methods Evidence on kangaroo mother care including evidence-based guidelines, best practices, systematic evaluation and so on were systematically retrieved from databases such as UpToDate, Cochrane Library, Ontario Registered Nurses Association of Canada, National Institute for Health and Care Optimization of the United Kingdom and National Guidelines Network of the United States from the establishment of the database to December 15th, 2018. Four researchers were divided into two groups and evaluated the quality of literature and extracted data. Results Eight papers were included, including one guideline and seven systematic reviews. The best evidence included suitable population, support system, skin contact (timing, duration, operator) , breast feeding support, etc. After evidence extraction, evidence aggregationin combination with localized adjustment in clinical situation, this study has formed eight best evidences. Conclusions It is necessary to strengthen and support the education of relative knowledge about kangaroo mother care, improve the facilities and promote the development of clinical kangaroo mother care based on the evidence and consider the special humanistic attributes with clinical environment at the same time . Key words: Infant, premature; Guidelines; Kangaroo mother care; Skin-to-skin contact; Systematic review; Evidence-based nursing; Best evidence
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".