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Socio-Economic Factors and Growth Ratardation in a Sub-Quarter of Abidjan Cocody Angré (Ivory Coast)

2017· article· en· W2750090331 on OpenAlexaboutno aff
Egnon K. V. Kouakou, Siaky Motihé Kamara, V Zannou-Tchoko, Kouakou Firmin Kouassi, Kouamé G. M. Bouafou, Coulibaly Amed, Massara Cisse-Camara, Alassane Meïté, Yoro Blé Marcel, Kacou J. M. Djetouan, Bruno K. Koko, Niaba Koffi Pierre Valery, Séraphin Kati-Coulibaly

Bibliographic record

VenueScience Journal of Public Health · 2017
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
Fundersnot available
KeywordsAnthropometryCote d ivoireMalnutritionEnvironmental healthQuarter (Canadian coin)SocioeconomicsGeographyUnder-fiveMedicineDemographyEconomicsHumanities

Abstract

fetched live from OpenAlex

The aim of this work is to study the socio-economic factors in relation to the stunted growth in children from 6 to 59 months in a suburb of Abidjan Cocody Angre. To this end, a cross-sectional, descriptive and analytical study was conducted at the community-based health facility in Abidjan (Cocody-Angre) over a period of three months (August to October 2016). In the course of this study, 958 children and mothers / accompanying persons were consulted. Of these children, 58 were stunted. This study found that 53% of children with stunting had mothers aged between 20 and 29 years. Similarly, mothers whose income were comprised between 3 USD and 6 USD registered 67% of growth retardation. Data were collected during the study period through the availability of structure staff, the use of growth curve tables and exchanges with selected mothers using individual questionnaires. These anthropometric data have been determined and compared with international ones. These results should be supplemented by further studies to better define the scope of actions to effectively fight malnutrition among children in the Cocody Angre health area in Cote d'Ivoire.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.343
Teacher spread0.294 · 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.

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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