Self-reported impacts of the COVID-19 pandemic among people who use drugs: a rapid assessment study in Montreal, Canada
Notice bibliographique
Résumé
BACKGROUND: People who use drugs (PWUD) are at high risk of experiencing indirect harms of measures implemented to curb the spread of COVID-19, given high reliance on services and social networks. This study aimed to document short-term changes in behaviours and health-related indicators among PWUD in Montreal, Canada following declaration of a provincial health emergency in Quebec. METHODS: We administered a structured rapid assessment questionnaire to members of an existing cohort of PWUD and individuals reporting past-year illicit drug use recruited via community services. Telephone and in-person interviews were conducted in May-June and September-December 2020. Participants were asked to report on events and changes since the start of the health emergency (March 13, 2020). Descriptive analyses were performed. RESULTS: A total of 227 participants were included (77% male, median age = 46, 81% Caucasian). 83% and 41% reported past six-month illicit drug use and injection drug use, respectively. 70% of unstably housed participants reported increased difficulty finding shelter since the start of the health emergency. 48% of opioid agonist treatment recipients had discussed strategies to avoid treatment disruptions with providers; 22% had missed at least one dose. Many participants perceived increased difficulty accessing non-addiction health care services. Adverse changes were also noted in indicators pertaining to income, drug markets, drug use frequency, and exposure to violence; however, many participants reported no changes in these areas. Among persons reporting past six-month injection drug use, 79% tried to access needle-syringe programmes during the health emergency; 93% of those obtained services. 45% tried to access supervised injection sites, of whom 71% gained entry. CONCLUSIONS: This snapshot suggests mixed impacts of the COVID-19 pandemic on PWUD in Montreal in the months following declaration of a provincial health emergency. There were signals of increased exposure to high-risk environments as well as deteriorations in access to health services. Pandemic-related measures may have lasting impacts among vulnerable subgroups; continued monitoring is warranted.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».