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Record W3001718831 · doi:10.1111/add.14950

Commentary on Ho <i>et al</i>. (2020): Managing people with HIV and drug problems is complex and multi‐faceted

2020· letter· en· W3001718831 on OpenAlexaboutno aff
Michael Farrell, Roy Robertson

Bibliographic record

VenueAddiction · 2020
Typeletter
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyMedicinePublic healthStigma (botany)Human immunodeficiency virus (HIV)Mental healthPublic relationsPsychiatryPsychologyPolitical scienceEconomic growthNursingFamily medicineEconomics

Abstract

fetched live from OpenAlex

International literature reviews can provide important insights into global drug use and HIV infection treatment. Management of HIV-infected individuals with coexisting drug problems requires a range of services, including medical prescribing, but crucially these must be engaged, sympathetic and supportive of those people who are estranged and marginalized. This paper by Ho et al. 1 is important, because it gives us an insight into the complexity of managing people who inject drugs and the impact and influence of life-style, poverty, adversity and the current limitations of public health policy in many, if not most, countries. The importance of life-style and economic stresses and the consequent need to accommodate difficulties in these areas should not need to be highlighted, but they are clearly an essential, and frequently missed, part of public health policy and clinical practice when delivering critical treatment to people constrained by drug use difficulties. The paper also draws attention to how people who use drugs are perceived and supported. In doing so, it strikes at the heart of many challenges for service delivery 1. These cluster around drug policy, stigma inhibiting change, variations between administrations, multi-drug use complicating risks, comorbid mental health problems and the dynamics between identifying the evidence supporting treatment and translating this into actions in affected communities. An additional message that we might extrapolate is the importance of people who inject drugs (PWID) for the impact of the world-wide HIV/AIDS epidemic, which is now driven by drug injecting in many countries (especially in Asia and increasingly in Africa) 2. Adherence to anti-retroviral therapy is constrained, not surprisingly, by ongoing drug use and various other factors that alert us to the focused attention needed to provide quality treatment services delivered with compassion and sufficient resources. The lessons of the last 30 years are that administrations experiencing drug use epidemics and managing and preventing HIV always seem to struggle to implement effective interventions such as medically assisted treatment (MAT) and needle and syringe availability. A binary approach between recovery/abstinence and harm reduction is over-simplistic, and treatment services need to be much more nuanced, personal and inclusive 3. Despite an extensively researched and widely published literature drawing attention to the benefits of engagement and retention in MAT programmes 4, 5, political decision-making and consensus always seem to lag behind or, in many cases, obstruct clinical practice. Crises generate change, however 6, and there are many examples of drug problems having dramatic effects as unexpected problems require new approaches. The United Kingdom changed drug policy in the 1980s in response to HIV infection and, more recently, the United States and Canada have had to make difficult political decisions in the wake of an epidemic of fentanyl and oxycodone use and overdose 7, 8. Indeed, the rising number of drug-related deaths in many countries is currently a major driver of drug policy change around the world, as it affects life expectancy and challenges conservative policy 9, 10. The changes made need to destigmatize people who use drugs and to reduce the barriers to treatment access by increasing the targeted funding to support treatment services, encouraging research and inclusive practice. None.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.025
GPT teacher head0.280
Teacher spread0.255 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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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