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Record W4232322759 · doi:10.24124/2020/59056

Support for people living with HIV: insights into knowledge acquisition and personal wellbeing

2020· dissertation· en· W4232322759 on OpenAlexaff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Northern British Columbia
FundersCenters for Disease Control and Prevention
KeywordsHuman immunodeficiency virus (HIV)Peer supportMedicineSocial supportGerontologyPsychologyNursingFamily medicineSocial psychology

Abstract

fetched live from OpenAlex

The provision of care for people living with Human Immunodeficiency Virus (HIV) has advanced since the 1980’s. New treatments have changed HIV to a chronic condition instead of a death sentence. How this change has affected support networks providing care to those living with HIV requires further investigation. Through interviews with Key Informants (n=4), and Family and Peer Support Networks for those living with HIV (n=7) three major themes emerged: 1) People providing support for people living with HIV are often HIV positive themselves. 2) Methods of learning about HIV/AIDS utilized before and after HIV diagnosis; such as, doctors, pamphlets, and others living with HIV. 3) Methods of support provided and received while living with HIV. These themes demonstrate the collaboration between support networks for people living with HIV. This research provides a greater understanding of support networks affected by and living with HIV.

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)0.003
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0040.005
Open science0.0000.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.317
Teacher spread0.304 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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