A structured classification of the types of pain research studies accessed by different health professionals involved in pain management
Notice bibliographique
Résumé
OBJECTIVES: The aim of this study was to describe the information access behaviours of clinicians involved in pain management with respect to their use of a pain evidence resource and to determine the areas of professional differences. METHODS: ) were enrolled in this study. The users regularly received email alerts about newly published clinical articles about pain that were pre-appraised for scientific merit and clinical relevance. A sample of up to 10 abstracts retrieved by each user were retrieved and classified using a descriptive classification system to describe the types of research, pain subtypes, interventions and outcomes that were reported in the accessed studies. Frequencies and chi-square tests were performed to compare access behaviours across professions. RESULTS: A total of 258 participants viewed 2311 abstracts. More than 52% of abstracts viewed were primary clinical studies; the majority (87%) addressed treatment effectiveness and were quantitative research (99.8%). The most commonly accessed clinical topic (58%) related to musculoskeletal pain and the most accessed pain type was chronic pain (76%). Drugs, injections and rehabilitation therapy were most commonly addressed in accessed intervention studies. Differences in professional focus were reflected in access: physicians/nurses accessed studies on injections (23%) and drugs (26%) and nurses accessed surgical studies, whereas other professions rarely did. Physiotherapists (PTs) and occupational therapists (OTs) preferentially accessed studies on rehabilitation. OTs and psychologists preferentially accessed the available studies on cognitive interventions; OTs accessed more ergonomic studies. Psychologists most accessed educational and psychosocial intervention studies. There were no differences in access across professions to multidisciplinary interventions. CONCLUSION: While access partially reflects the content of the pain repository, professional differences in access were evident that related to the nature of the intervention, type of pain and the research design. Multidisciplinary evidence repositories may need to consider how to include and meet varied information needs.
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 | 0,068 | 0,006 |
| 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 | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».