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Record W2883045284 · doi:10.2989/16085906.2018.1478313

“Bringing back hope”: how faith-based responses to HIV and AIDS differ from secular responses

2018· article· en· W2883045284 on OpenAlexaff
Deborah Simpson

Bibliographic record

VenueAfrican Journal of AIDS Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsFaithSpiritualitySociologyPsychological interventionArgument (complex analysis)Human immunodeficiency virus (HIV)Gender studiesGovernment (linguistics)AssertionInterviewMedicineNursingFamily medicineAlternative medicineTheologyAnthropology

Abstract

fetched live from OpenAlex

This article investigates an assertion by faith-based organisations (FBO) that spirituality is the defining feature of their HIV and AIDS interventions. It is based on interviews with 24 people working on the issue of HIV and AIDS in churches or church organisations in Johannesburg, Rustenburg, Pretoria, Durban, Pietermaritzburg, and Cape Town. The article critically assesses the perceived difference between faith-based responses to HIV and AIDS and secular responses, including government programmes, in relation to the research literature on spirituality. After introducing the article, the argument begins with an exploration of the literature on churches and HIV and AIDS, outlining a gap which the article seeks to fill. The article then discusses the methods used for interviewing and analysing interview material. This is followed by religious leaders' own comments on how faith-based responses to HIV and AIDS differ from secular responses. The article concludes with a discussion which brings the literature to bear on the interview excerpts and then outlines the implications of decreased international funding for the HIV and AIDS programmes operated by the Anglican and Catholic churches, for example, a likely reduction in the accompaniment and monitoring of those who are HIV-positive.

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)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.065
GPT teacher head0.376
Teacher spread0.311 · 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 designNot applicable
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

Citations2
Published2018
Admission routes1
Has abstractyes

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