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Enregistrement W3009671199 · doi:10.1371/journal.pone.0229713

Illicit drug use while admitted to hospital: Patient and health care provider perspectives

2020· article· en· W3009671199 sur OpenAlexafffundabout
Carol Strıke, Samantha Robinson, Adrian Guţă, Darrell H. S. Tan, Bill O’Leary, Curtis Cooper, Ross Upshur, Soo Chan Carusone

Notice bibliographique

RevuePLoS ONE · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueOpioid Use Disorder Treatment
Établissements canadiensMcMaster UniversitySt. Michael's HospitalCasey HouseUniversity of OttawaUniversity of WindsorPublic Health OntarioUniversity of Toronto
Organismes subventionnairesCanadian Institutes of Health ResearchOntario HIV Treatment Network
Mots-clésMedicineHealth careFamily medicineFocus groupMedical emergencyNursing

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Across North America, the opioid overdose epidemic is leading to increasing hospitalizations of people who use drugs (PWUD). However, hospitals are ill-prepared to meet the needs of PWUD. We focus on illicit drug use while admitted to hospital and how PWUD and health care providers describe, respond, and attempt to manage its use. METHODS AND FINDINGS: Using varied purposive methods in Toronto and Ottawa, we recruited n = 24 PWUD (who self-reported that they were living with HIV and/or HCV infection; currently or had previously used drugs or alcohol in ways that were harmful; had a hospital admission in the past five years) and n = 26 health care providers (who were: currently working in an academic hospital as a physician, nurse, social worker or other allied health professional; and 2) providing care to this patient group). All n = 50 participants completed a short, socio-demographic questionnaire and an audio-recorded semi-structured interview about receiving or providing acute care in a hospital between 04/2014 and 05/2015. Patient participants received $25 CAD and return transit fare; provider participants received a $50 CAD gift card for a bookseller. All participants provided informed consent. Audio-recordings were transcribed verbatim, corrected, and uploaded to NVivo 10. Using the seven-step framework method, transcripts were coded line-by-line and managed using NVvivo. An analytic framework was created by grouping and mapping the codes. Preliminary analyses were presented to advisory group members for comment and used to refine the interpretation. Questionnaire data were managed using SPSS version 22.0 and descriptive statistics were used to describe the participants. Many but not all patient participants spoke about using psycho-active substances not prescribed to them during a hospital admission. Attempts to avoid negative experiences (e.g., withdrawal, boredom, sadness, loneliness and/or untreated pain) were cited as reasons for illicit drug use. Most tried to conceal their illicit drug use from health care providers. Patients described how their self-reported level of pain was not always believed, tolerance to opioids was ignored, and requests for higher doses of pain medications denied. Some health care providers were unaware of on-site illicit drug use; others acknowledged it occurred. Few could identify a hospital policy specific to illicit drug use and most used their personal beliefs to guide their responses to it (e.g., ignore it, increase surveillance of patients, reprimands, loss of privileges/medications, threats of immediate discharge should it continue, and substitution dosing of medication). CONCLUSIONS: Providers highlighted gaps in institutional guidance for how they ought to appropriately respond to in-hospital substance use. Patients attempted to conceal illicit drug use in environments with no institutional policies about such use, leading to varied responses that were inconsistent with the principles of patient centred care and reflected personal beliefs about illicit drug use. There are increasing calls for implementation of harm reduction approaches and interventions in hospitals but uptake has been slow. Our study contributes to this emerging body of literature and highlights areas for future research, the development of interventions, and changes to policy and practice.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,465
Score d'incertitude au seuil0,581

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,038
Tête enseignante GPT0,257
Écart entre enseignants0,219 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

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

En bref

Citations79
Publié2020
Routes d'admission3
Résumé présentoui

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