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Record W4396217615 · doi:10.1071/sh24028

The use of suboptimal antiretroviral therapy when applying for migration to Australia: a case series

2024· article· en· W4396217615 on OpenAlexaff
Daniel Tran, Brent Allan, Alexandra Stratigos, Darryl O’Donnell, Dash Heath‐Paynter, Aaron Cogle, Jason J. Ong

Bibliographic record

VenueSexual Health · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsNevirapineMedicineEfavirenzAntiretroviral therapyPublic healthAnxietyFamily medicineHuman immunodeficiency virus (HIV)PsychiatryViral loadNursing

Abstract

fetched live from OpenAlex

Background Australia imposes restrictions for people living with HIV (PLHIV) applying for permanent residency (PR), including spending less than AUD51,000 on medical costs over 10years. Some PLHIV opted for suboptimal and cheaper antiretroviral therapy (ART) regimens to increase their chances of receiving PR. We collated a case series to examine PLHIV on suboptimal ART because of visa issues. Methods We identified all patients applying for a PR in Australia who obtained nevirapine, efavirenz or zidovudine between July 2022 and July 2023 from the Melbourne Sexual Health Centre. Pathology results and records detailing psychological issues relating to the patients' wishes to remain on suboptimal ART were extracted from clinical records by two researchers. Results We identified six patients with a mean age of 39years migrating from Asian and European countries. Three patients used efavirenz, and three used nevirapine. All desired to remain on cheaper, suboptimal ART to stay below visa cost thresholds, which they considered to aid favourably with their application. Four displayed stress and anxiety arising from visa rejections, appeal deadlines and the lengthy visa application process. Conclusions Despite access to more effective and safer ART, we identified patients who chose to remain on cheaper ART to improve chances of obtaining an Australian visa, potentially putting their health at risk. We found significant evidence of stress and anxiety among patients. There is a need to review and revise current migration policies and laws in Australia that discriminate against PLHIV and jeopardise public health.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.214
GPT teacher head0.453
Teacher spread0.239 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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