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Enregistrement W3094952772

Learning in the Midst of a Crisis: Understanding Internal Medicine Residents’ Knowledge and Comfort in Caring for Patients With Opioid Use Disorders

2020· dissertation· en· W3094952772 sur OpenAlexaboutno aff
G.H. Inglis

Notice bibliographique

RevueDigital Access to Scholarship at Harvard (DASH) (Harvard University) · 2020
Typedissertation
Langueen
DomaineMedicine
ThématiqueOpioid Use Disorder Treatment
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOpioidMedicineOpioid epidemicPsychiatryPsychologyInternal medicine
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Introduction: In the past five years, the Canadian health care system has faced a growing crisis of opioid overdoses (Government of Canada, 2018). This crisis has received attention not only from public health and government officials, but also from medical educators responsible for determining what and how trainees learn about opioid use disorders and their management (AFMC, 2018). Little is known about the needs of learners in terms of knowledge, attitudes, and skills in the diagnosis and management of opioid use disorders to guide curriculum innovation in this area. Objectives: This study sought to evaluate the knowledge and comfort of Internal Medicine residents at the University of Toronto with respect to diagnosis and management of opioid use disorders using a mixed-methods design. The specific study aims were: 1) to describe any gaps in internal medicine resident knowledge and comfort with respect to recognition and management of opioid use disorders; 2) To better understand those factors that residents perceive to positively and negatively impact their knowledge and comfort in caring for this population; and 3) to generate data that will help guide educational interventions to improve both formal and informal curricula and enable trainees to better meet the needs of this population. Methods: This study was designed as a mixed-methods study, using a sequential explanatory design in which the quantitative portion (a survey, described here) would precede and guide the qualitative portion (interviews with residents to be conducted as future work). Results: A total of 14 residents completed the survey, with a response rate of 7%. The majority of Internal Medicine residents who participated in this survey study were able to correctly diagnose an opioid use disorder in a written clinical scenario, recognize the symptoms of acute opioid withdrawal, and offer appropriate medications for its management. Most were able to correctly name both first- and second-line options for opioid agonist treatment. Despite the high numbers of correct responses to knowledge-related questions, only half of respondents reported feeling comfortable making a diagnosis of an opioid use disorder, and a minority of respondents felt comfortable with the principles of prescribing either buprenorphine/naloxone or methadone. Similarly, only half of respondents were comfortable recognizing signs of acute opioid withdrawal, and less than half were comfortable with its management. Conclusions: This study is limited by non-random sampling, a low survey response rate and small sample size. However, these results generate interesting questions about the extent to which knowledge alone may not predict comfort in caring for patients with opioid use disorders. Insofar as high levels of knowledge amongst respondents might reflect self-selection bias, we might also expect these respondents to feel more comfortable than their non-responding peers. Thus, the generally low comfort levels of this sample of residents leads one to question whether comfort levels among non-responders might be even lower – a possibility that would have important implications for curriculum development in this area. Another possibility is that the high levels of knowledge, and comparatively low levels of comfort are a reflection of the Dunning-Kruger effect. A larger sample size with linear regression modeling of the relationship between knowledge, self-rating of that knowledge, and comfort could help explore these theories further and generate data to guide curricular interventions.

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)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,035
Score d'incertitude au seuil0,070

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,003
Communication savante0,0020,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,033
Tête enseignante GPT0,279
Écart entre enseignants0,245 · 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'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

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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