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Record W7132939183

Assez d’enfants: Reproduction, risque et besoin « nonsatisfait » chez les gens qui suivent un traitementantirétroviral en Ouganda de l’Ouest

2012· article· en· W7132939183 on OpenAlexaff
Amy Kaler, Arif Alibhai, Walter Kipp, Joseph Konde-Lule, Rubaale. Tom

Bibliographic record

VenueTSpace · 2012
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsCapital District Health Authority
Fundersnot available
KeywordsHappinessBirth controlPerceived controlPerceptionControl (management)Risk perceptionReproductive health
DOInot available

Abstract

fetched live from OpenAlex

In this paper, we use survey (n=87) and interview (n=30) data to investigate orientations towards future childbearing among people receiving antiretroviral treatment and their family members in western Uganda. We investigate how reproductive options are perceived, by those receiving treatment and those closest to them, and consider what these perceptions suggest about the existence of an “unmet need” for birth control for women with HIV. While most people say they do not wish to have more children while on treatment, this intention coexists with contradictory desires for the benefits and happiness that more children might bring. We argue that the factors influencing birth desires and outcomes are so complex and contradictory that it is virtually impossible to predict demand or uptake of birth control as more and more people with AIDS in Africa gain the ability to access antiretroviral treatments (Afr J Reprod Health 2012; 16[1]:133-144).

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.028
GPT teacher head0.365
Teacher spread0.337 · 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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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