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Record W4411545506 · doi:10.1016/j.wss.2025.100280

“Cold turkey meds again”: Access to community HIV/AIDS services during public health emergencies

2025· article· en· W4411545506 on OpenAlexafffundabout
Darby Whittaker, Erynn M. Monette, Miesha Polintan, Elijah Bisung

Bibliographic record

VenueWellbeing Space and Society · 2025
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsPublic Health OntarioQueen's University
FundersQueen's University
KeywordsHuman immunodeficiency virus (HIV)Public healthMedicineHealth servicesFamily medicineMedical emergencyNursingEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

This paper works to explore the experiences of people living with HIV (PLWH) in relation to the closure of AIDS Service Organizations (ASOs) in Ontario, Canada, during the COVID-19 pandemic and offers recommendations on how to ensure a continuum of care for PLWH during public health emergencies like COVID-19. Semi-structured in-depth interviews were conducted with PLWH (n=8) and ASO service providers (n=8). Participants discussed their experiences with HIV services throughout the pandemic. Of the PLWH interviewed, individuals shared experiences of worsening mental health outcomes, difficulties accessing resources, disruption in medical care, and increased feelings of isolation. ASO service providers identified changes in their functions, increased barriers in service provision, and staff fatigue as challenges to their work. The results of this study demonstrate the need for reimagining HIV/AIDS and other service provisions during pandemics to ensure that resources remain accessible for PLWH and other marginalized populations. Essential ASO services to maintain a continuum of care during pandemic circumstances include prioritizing mental health supports, regular access to nutritious food, clothing, and financial support, and consistent check-ins between clients and service providers.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.033
GPT teacher head0.353
Teacher spread0.320 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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