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Record W2914737710

Percepciones sobre embarazo y lactancia materna en adolescentes embarazadas de San Luis Potosí, México

2018· article· es· W2914737710 on OpenAlexaboutno aff
Estela Gámez Hernández, Sulima del Carmen García-Falconi, María Eugenia Pérez-Robledo, María Laura López-Torres, María Teresa Guerrero-Hernández, Rosa Corpus-Cabriales

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsBreastfeedingMedicinePregnancyQuarter (Canadian coin)Prenatal careFamily medicineObstetricsPediatricsPopulationEnvironmental healthGeography
DOInot available

Abstract

fetched live from OpenAlex

Objective: Describe the perceptions on pregnancy and breastfeeding in pregnant teenagers. Material and Methods: The sample consisted of pregnant teenagers registered in prenatal consultation. Interviews were recorded and transcribed literally, previously authorized informed consent. Results: Fifteen pregnancy women were studied, 86% with first pregnancy, between 15 - 19 years old, coursing the last trimester. Pregnancy became important in street situations and the use of illegal drugs. The perceptions of breastfeeding were based on tradition and gender forms. All teenagers agreed on breastfeeding from two months to two years old. A quarter of the teenagers planned to take care of their children and family, and three quarters went to finish school and start working. Family support had a significant impact on teenagers to face this process. Conclusions: Teenagers identified the practice of breastfeeding as the irreplaceable nourishment in the infant's life, which reflects strengthening of knowledge during the prenatal consultation. Key words: Pregnancy, breastfeeding, teenagers.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.013
GPT teacher head0.298
Teacher spread0.285 · 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 designQualitative
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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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