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Clinician-Initiated HIV Risk Reduction Intervention for HIV-Positive Persons

2004· article· en· W1983496465 on OpenAlexaff
Jeffrey D. Fisher, Deborah H. Cornman, Chandra Y. Osborn, K. Rivet Amico, William A. Fisher, Gerald A Friedland

Bibliographic record

VenueJAIDS Journal of Acquired Immune Deficiency Syndromes · 2004
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsWestern University
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)MedicineIntervention (counseling)VirologyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To conduct research on levels and dynamics of HIV risk behavior among HIV-positive patients in clinical care, use this research to design a clinician-initiated HIV prevention intervention for HIV-positive patients, and evaluate the acceptability of the intervention to clinicians and patients and the fidelity with which it can be delivered by clinicians. METHODS: Study 1 (elicitation research) involved focus groups with HIV-positive patients and HIV care clinicians to understand the dynamics of HIV risk behavior among HIV-positive patients and how to integrate HIV prevention into routine clinical care. Study 2 (acceptability and intervention fidelity) involved the evaluation of 1455 medical visits by experimental intervention patients (N = 231) for acceptability and fidelity of the clinician-initiated HIV prevention intervention. RESULTS: Elicitation research with patients and clinicians identified critical HIV prevention information, motivation, and behavioral skills deficits in HIV-positive patients as well as risky sexual behavior. These findings were integrated into a theory-based HIV prevention intervention initiated by clinicians that proved acceptable to clinicians and patients and that clinicians were able to implement with adequate fidelity. CONCLUSION: HIV prevention interventions by clinicians treating HIV-positive patients can and should be integrated into routine clinical care.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.036
GPT teacher head0.350
Teacher spread0.314 · 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 designBench or experimental
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

Citations104
Published2004
Admission routes1
Has abstractyes

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