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Record W3164224151 · doi:10.1016/j.anpedi.2021.04.010

Prácticas de lactancia materna en las unidades neonatales de España. Encuesta internacional Neo-IHAN

2021· article· es· W3164224151 on OpenAlexaff
Marta Cabrera‐Lafuente, Clara Alonso Díaz, María Teresa Moral Pumarega, Mariana Díaz‐Almirón, Laura N. Haiek, Ragnhild Maastrup, Carmen Rosa Pallás‐Alonso

Bibliographic record

VenueAnales de Pediatría · 2021
Typearticle
Languagees
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsMcGill UniversityMinistry of Health and Social Services
Fundersnot available
KeywordsHumanitiesPolitical scienceMedicinePhilosophy

Abstract

fetched live from OpenAlex

En 2017 se realizó una encuesta a nivel mundial sobre el cumplimiento de las prácticas que promueve la Neo-IHAN (Iniciativa para la Humanización de la Asistencia al Nacimiento y la Lactancia en las unidades neonatales). Presentar los resultados de las unidades españolas que participaron en la encuesta mundial y compararlos con los obtenidos internacionalmente. Estudio transversal mediante una encuesta sobre el cumplimiento de los requisitos de la Neo-IHAN («Tres principios básicos», «Diez pasos adaptados a unidades neonatales» y el «cumplimiento del Código internacional de comercialización de sucedáneos de leche materna»). El cumplimiento se calculó como la media en cada indicador y una puntuación media final para cada unidad neonatal. Para las puntuaciones parciales y finales de cada país y a nivel internacional se utilizó la mediana. Las puntuaciones van de 0 a 100. La tasa de respuesta en España fue del 90% de las unidades de nivel 2 y 3. El rango de la media para las unidades neonatales fue de 37 a 99, sin diferencias según el nivel asistencial. La puntuación global de España (72) está por debajo de la mediana internacional (77), así como en 8 de los 14 requisitos de la Neo-IHAN. Las unidades neonatales de hospitales con maternidades acreditadas IHAN obtuvieron una puntuación media final significativamente mayor, así como en 9 de los 14 requisitos frente a las no acreditadas. Los resultados, tanto internacionales como nacionales, indican una mejora de las prácticas de la lactancia materna en las unidades neonatales. Los beneficios de la acreditación IHAN de las maternidades alcanzan a las unidades neonatales. España tiene varios puntos clave por debajo de la puntuación internacional. In 2017, a worldwide survey was conducted on compliance with the practices promoted by Neo-BFHI (Baby-friendly Hospital Initiative expansion to neonatal wards). Present the results of the Spanish wards that participated in the global survey and compare them with those obtained internationally. Cross-sectional study through a survey on compliance with the Neo-BFHI (“Three basic principles”, “Ten steps adapted to neonatal wards” and “the compliance with the International Code of Marketing of Breast-milk Substitutes” and subsequent relevant World Health Assembly resolutions). Compliance was calculated as the mean in each indicator and a final mean score for each neonatal unit. For the partial and final scores for each country and at the international level, the median was used. All score ranged between 0 and 100. The response rate in Spain was 90%. The range of the national mean for neonatal wards were from 37 to 99, with no differences in the final score according to the level of care. The global score for Spain (72) is below the international median (77) and this also occurs in 8 of 14 items. The neonatal wards from BFHI designated hospitals, obtained a significantly higher mean global score, and in 9 of 14 items than the non-accredited ones. Both international and national results indicate an improvement in breastfeeding practices in neonatal units. The benefits of the BFHI accreditation of maternity reach neonatal wards. Spain has several key points below the international score.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.011
GPT teacher head0.303
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations6
Published2021
Admission routes1
Has abstractyes

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