Illicit drug use while admitted to hospital: Patient and health care provider perspectives
Notice bibliographique
Résumé
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.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».