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Record W1483678506

La promoción de salud dirigida a reducir los factores de riesgo de cáncer cérvico uterino

2014· article· es· W1483678506 on OpenAlexaboutno aff
Misleny Martínez Pérez, Juan Carlos de la Concepción Cárdenas, Ariel Pérez González

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2014
Typearticle
Languagees
FieldSocial Sciences
TopicPublic Health and Social Inequalities
Canadian institutionsnot available
Fundersnot available
KeywordsHealth promotionAcknowledgementMedicineCervical cancerHealth educationHealth carePopulationPromotion (chess)NursingGynecologyCancerPublic healthEnvironmental healthPolitical science
DOInot available

Abstract

fetched live from OpenAlex

La primera Conferencia Internacional sobre la Promoción de la Salud celebrada en Ottawa, el 21 de noviembre de 1986, definió la promoción de la salud como “el proceso de permitir a la gente aumentar su control sobre su salud y por lo tanto mejorarla”. Se reconoce claramente que la promoción de la salud va más allá de los estilos de vida, en la cual la educación para la salud constituye, junto a la comunicación y a la participación social, herramientas necesarias para su instrumentación en el primer nivel de atención. En la práctica cotidiana se desconoce de forma reiterada el valor que tiene el reconocimiento adecuado de las dimensiones de la promoción de salud, desde una perspectiva holística, dirigidas a reducir los factores de riesgo de cáncer cérvico uterino. El propósito del estudio fue realizar una contribución teórica a la promoción de salud para contribuir al fortalecimiento del Programa Nacional de Diagnóstico Precoz del Cáncer Cérvico Uterino. Al considerar el valor de esta disciplina desde sus dimensiones, el profesional de la salud dispone de una base teórica que guíe las acciones dirigidas a la reducción de factores de riesgos de este tipo de cáncer en la población femenina.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0060.003
Open science0.0050.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.297
GPT teacher head0.628
Teacher spread0.331 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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