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Record W3024048759

Distribution Spatiale et Taille des Populations Adolescentes et Jeunes en Situation de Vulnérabilité au VIH au Cameroun

2020· article· fr· W3024048759 on OpenAlexaboutno aff
SC Billong, LB Savadogo, H. Mbwolie Nsabala, CI Penda, MN Ngoufack, Désiré Akaba, Antonio Lorenzo Tena, D Ngouo, AF Zeh Meka, Joseph Fokam, Brian Bongwong Tamfon, EJ Billong, JB Guiard-Schmid, R Moutapam B Pamela, H Diallo, Leonard Bonono, AC Zoung-Kanyi Bissek, Georges Nguefack‐Tsague

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsnot available
Fundersnot available
KeywordsIncidence (geometry)Human immunodeficiency virus (HIV)Context (archaeology)DemographyMedicineVulnerability (computing)PopulationGeographyEnvironmental healthImmunologySociology
DOInot available

Abstract

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ABSTRACT Introduction. The highest incidence of HIV in Cameroon is found among adolescents and young people (teenagers). The country, however, devotes significant preventive investments for this group. For an efficient and effective implementation of the fight against HIV in this group, it is essential to study the spatial and demographic distribution of these populations in a context of vulnerability to HIV in Cameroon. Methods. This cross-sectional and descriptive study was conducted in the ten regions of Cameroon. The approach used was that of programmatic mapping developed by the University of Manitoba (Canada). Adolescents and youths at risk of HIV were people aged 10 to 24 years in relation to key populations (KPs) such as: sex workers, men who have sex with men, and drug users; either on (or near) hot spots; or living with a KP outside the hot spots. Results. A total of 3 425 hotspots were identified. The number of vulnerable adolescents was estimated at 201 653 (155 615-247 691). Littoral, Centre, and Western regions had the highest number with 37 442 (29 015-45 828), 32 917 (26 901-38 932) and 24 472 (18 332-30 613) respectively. Conclusion. These results suggest that preventive measures should be developed for adolescents, as well as the conduct of Integrated Biological and Behavioral Surveillance (IBBS) to better understand their vulnerabilities, their needs and the extent of HIV in this group. RESUME Introduction. La plus forte incidence du VIH au Cameroun se retrouve chez les adolescent(e)s et jeunes. Le pays consacre pourtant des investissements de prevention importants pour ce groupe.  Pour une mise en œuvre efficace et efficiente de la lutte contre le VIH dans ce groupe, il est indispensable d’etudier la distribution spatiale et demographique de ces populations en contexte de vulnerabilite au VIH au Cameroun. Methodes. Cette etude transversale et descriptive a ete realisee dans les dix regions du Cameroun. L’approche utilisee a ete celle de la cartographie programmatique developpee par l’Universite de Manitoba (Canada). Les adolescent(e)s et jeunes vulnerables au VIH etaient des personnes âgees entre 10 et 24 ans en lien avec les populations cles (PC) telles que : Travailleuses de sexe, Hommes ayant des rapports sexuels avec des hommes, et Usagers de drogues; soit sur (ou proximite) les points chauds (PCd); ou vivant avec une PC en dehors du PCd. Resultats. Au total 3 425 PCds ont ete identifies. Le nombre des adolescent(e)s et jeunes vulnerables etait estime a 201 653 (155 615-247 691). Les regions du Littoral, du Centre et de l’Ouest comptaient le plus grand nombre avec respectivement 37 442 (29 015-45 828), 32 917 (26 901-38 932), et 24 472 (18 332-30 613). Conclusion. Ces resultats suggerent de developper des interventions specifiques de prevention chez les adolescent(e)s et jeunes autour des « points chauds », ainsi que la conduite d’etudes bio-comportementales dans ce groupe, afin de mieux comprendre l’envergure et les determinants de leur vulnerabilite.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.662
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.088
GPT teacher head0.310
Teacher spread0.222 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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