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Enregistrement W1128796040 · doi:10.11575/prism/10660

DynIA: Dynamically Informed Allegories

2015· article· en· W1128796040 sur OpenAlexaboutno aff
David Topps, Paul Taenzer, Heather Armson, Eloise Carr

Notice bibliographique

RevuePRISM (University of Calgary) · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueMusculoskeletal pain and rehabilitation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésComputer science

Résumé

récupéré en direct d'OpenAlex

An important strategy for improving population health is to use what we learn from medical research in our patient care. One approach to this is using the highest quality medical research to make recommendations and guide healthcare providers in deciding how to diagnose and treat their patients. These recommendations form the basis of healthcare tools that are called clinical practice guidelines. Theme four focused on strategies for increasing the uptake of clinical practice guidelines on low back pain and headache into community-based care. Theme four researchers collaborated with guideline developers in Alberta at the Institute of Health Economics and an organization called Towards Optimize Practice (TOP) that is sponsored by the Alberta Medical Association and the Alberta Ministry of Health (Alberta Health and Wellness). The research team first looked at what is already been known about uptake of guideline recommendations for chronic pain. This process involved going back to original research from around the world. Research librarians and scientists found 19 scientific papers that are relevant. Taken together, these studies indicated that the best approach to improving uptake of chronic pain guidelines into community care is to present them to care providers in special interactive educational settings where they are able to discuss the recommendations approaches with the educators. Theme four then went on to test this approach in the study of using an interactive educational workshop focused on the low back pain guideline. The study was conducted in collaboration with researchers from the University of Calgary and the University of Alberta. The workshop presenters were an expert team of physicians, physiotherapists, nurses and psychologists that traveled to the offices of the community healthcare providers. This study showed that the providers’ knowledge of low back pain increased after the workshop. When the medical records were examined, the researchers were unable to detect changes in how care was provided. This was a small study involving 24 providers. The researchers concluded that a larger study may confirm the increase in provider knowledge and detect changes in care. An important advance in healthcare is the use of computerized medical records. Computerization also provides an opportunity for healthcare providers to access relevant health information during their time with the patient. Theme four researchers collaborated with the Department of Family Medicine that McMaster University to develop a tool to help community caregivers use the recommendations from clinical practice guidelines while they are in the office with patients. This tool called the McMaster Pain Assistant has undergone successful usability testing and is now being tested in the community to see if using the tool leads to increases in knowledge and decisions that reflect the guideline. Rural physicians face important challenges in accessing medical education. In the past they would have to leave their practices and travel to a distant site to learn. Theme four researchers collaborated with the Department of Continuing Medical Education at the University of Calgary to explore a distance learning approach using Internet-based webinars and “virtual patients” that are designed to teach about the guidelines and how it might affect their care. This preliminary study demonstrated that rural physicians appreciated being able to access high quality medical education where they can interact with experts without having to travel. They found the sessions and the virtual patients highly engaging and realistic. Only small changes were shown in management of the virtual patients through the case series. Detailed analysis of practice patterns showed participants to be very conformant with clinical practice guideline recommendations.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,026
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,076
Score d'incertitude au seuil0,254

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,026
Méta-épidémiologie (sens strict)0,0010,002
Méta-épidémiologie (sens large)0,0020,003
Bibliométrie0,0030,002
Études des sciences et des technologies0,0040,004
Communication savante0,0070,013
Science ouverte0,0040,014
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0760,013

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 Opus0,011
Tête enseignante GPT0,228
Écart entre enseignants0,217 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

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

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

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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