Narcotic analgesic utilization amongst injured workers: using concept mapping to understand current issues from the perspectives of physicians and pharmacists
Notice bibliographique
Résumé
BACKGROUND: Work-related injuries result in considerable morbidity, as well as social and economic costs. Pain associated with these injuries is a complex, contested topic, and narcotic analgesics (NA) remain important treatment options. Factors contributing to NA utilization patterns are poorly understood. This qualitative study sought to characterize the factors contributing to NA utilization amongst injured workers from the perspectives of physicians and pharmacists. METHODS: The study employed concept mapping methodology, a structured process yielding a conceptual framework of participants' views on a particular topic. A visual display of the ideas/concepts generated is produced. Eligible physicians and pharmacists (n = 22) serving injured workers in the province of Ontario (Canada) were recruited via purposive sampling, and participated in concept mapping activities (consisting of brainstorming, sorting, rating, and map exploration). Participants identified factors influencing NA utilization, and sorted these factors into categories (clusters). Next, they rated the factors on two scales: 'strength of influence on NA over-utilization' and 'amenability to intervention'. During follow-up focus groups, participants refined the maps and discussed the findings and their implications. RESULTS: 82 factors were sorted into 7 clusters: addiction risks, psychosocial issues, social/work environment factors, systemic-third party factors, pharmacy-related factors, treatment problems, and physician factors. These clusters were grouped into 2 overarching categories/regions on the map: patient-level factors, and healthcare/compensation system-level factors. Participants rated NA over-utilization as most influenced by patient-level factors, while system-level factors were rated as most amenable to intervention. One system-level cluster was rated highly on both scales (treatment problems - e.g. poor continuity of care, poor interprofessional communication, lack of education/support for physicians regarding pain management, unavailability of multidisciplinary team-based care, prolonged wait times to see specialists). CONCLUSIONS: Participants depicted factors driving NA utilization among injured workers as complex. Patient-level factors were perceived as most influential on over-utilization, while system-level factors were considered most amenable to intervention. This has implications for intervention design, suggesting that systemic/structural factors should be taken into account in order to address this important health issue.
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 | 0,001 | 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 | 0,000 |
| 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 ».