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Record W2012798907 · doi:10.1177/1473325009103379

HIV/AIDS Social Services and the Changing Treatment Context

2009· article· en· W2012798907 on OpenAlexafffundabout
Roy Cain, Sarah Todd

Bibliographic record

VenueQualitative Social Work · 2009
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCarleton UniversityMcMaster University
FundersOntario HIV Treatment Network
KeywordsSocial workPovertyHuman immunodeficiency virus (HIV)Context (archaeology)Service providerPublic relationsMedicineSocial WelfareSociologyNursingService (business)GerontologyEconomic growthPolitical scienceBusinessFamily medicine

Abstract

fetched live from OpenAlex

This article examines how medical advances of the past decade affect social services for people living with HIV. Data for the study were drawn from in-depth interviews with 59 social service providers in Ontario, Canada. New antiretroviral treatments help many people to live longer and healthier lives with HIV. As a result of the improved health of clients, the focus of much of the work of social service providers has changed from acute health concerns to more chronic social issues. HIV can be just one of many complex issues in the lives of clients living with HIV/AIDS, as workers increasingly confront social problems, such as poverty, inadequate housing, or unavailable drug treatment services. Workers may have little training or experience in dealing with such issues. The article describes how agencies and workers have had to adapt to new practice realities resulting from effective HIV treatments.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.501

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0230.024
Scholarly communication0.0070.003
Open science0.0010.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.247
GPT teacher head0.503
Teacher spread0.256 · 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 designQualitative
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

Citations7
Published2009
Admission routes3
Has abstractyes

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