Intervenciones de enfermería para prevenir trastornos nutricionales en niños de 0 a 2 años
Bibliographic record
Abstract
Colombia lives a nutritional transition situation where nursing professionals play an important role through the construction of interventions to prevent early childhood nutritional disorders; The objective is to build a guide to recommendations for the generation of prevention interventions in nutritional disorders in children under two years of age from nursing care; A documentary research was developed with secondary-based sources of information; The first 3 stages of the evidence-based nursing (BSE) are implemented; The tool of the Canadian Task Force on Preventive Health Care (CTFPHC) was used for the classification of the evidence and the elaboration of the recommendations; The available evidence refers to the fact that promoting the development of strategies aimed at ensuring optimal nutrition prior to conception in both men and women ensures a decrease in the presence of malnutrition in childhood. The application of BSE identifies the scientific evidence of quality that allows decision-making and the development of actions that can lead to the strengthening of the health status of the populations and to reduce the costs of health care beginning with the preventive approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".