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Enregistrement 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 sur OpenAlexaboutno aff
Michael Farrell, Roy Robertson

Notice bibliographique

RevueAddiction · 2020
Typeletter
Langueen
DomaineMedicine
ThématiqueHIV, Drug Use, Sexual Risk
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPovertyMedicinePublic healthStigma (botany)Human immunodeficiency virus (HIV)Mental healthPublic relationsPsychiatryPsychologyPolitical scienceEconomic growthNursingFamily medicineEconomics

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,013
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,025
Tête enseignante GPT0,280
Écart entre enseignants0,255 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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