Learning in the Midst of a Crisis: Understanding Internal Medicine Residents’ Knowledge and Comfort in Caring for Patients With Opioid Use Disorders
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".