Movie night! An entertaining online educational method for introducing students to common presentations in neurology
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has obliged many medical schools to provide exclusively online education. In the current climate, we sought to develop a novel and entertaining online educational method for introducing pre-clerkship students to common clinical presentations in neurology. At McGill University, second-year medical students undertake a 2-week course called Transition to Clinical Practice in Neurology. The learning objectives for this course include: (a) to demonstrate a sound approach to diagnosing and treating patients with common neurological problems, and (b) to outline the roles and responsibilities of the various health professionals and caregivers involved in the care of patients with neurological problems. To help students achieve these goals online, we designed an assignment in which we asked students to imagine that they were movie critics tasked with reviewing one of the following films: Awakenings (Parkinson's disease); Still Alice (Alzheimer's dementia); The Theory of Everything (Amyotrophic Lateral Sclerosis); The Diving Bell and the Butterfly (Stroke), and Motherless Brooklyn (Tourette's syndrome). In their review, students were requested to provide their thoughts on the accuracy of the protagonist's depiction of the neurological illness, on how the various characters portraying health professionals (physicians, nurses, social workers, and others) were represented, and on the reactions and responses of the family members, friends and caregivers of the character affected by neurological illness in the movie. On the last day of the course, students were invited to share and discuss the content of their written reviews with their peers (in groups of seven to eight) in tutor-facilitated online small group sessions. ‘Trigger films’ depicting patient-clinician encounters have been used in health professions education to provoke reflection, stimulate discussion and enhance learning.1 We tried to select films showing honest portrayals of common neurological diseases. As opposed to the short video clips of neurological disorders commonly available on YouTube (www.youtube.com), for example, full-length features provide students with opportunities to observe portrayals of neurological illness in a plausible biopsychosocial context. They also afford students opportunities to gain an appreciation for the complex relationships that develop between patients, health care professionals and the family members involved in their care. After viewing the movies, debriefing through small group discussion was important for examining these relationships, as well as for highlighting key elements of the portrayals that were either accurately or inaccurately represented. We found that students did a fair amount of independent reading in order to appraise the portrayals of the neurological disorders in the films. Although they were not explicitly instructed to reflect on the COVID-19 pandemic, several students drew insightful parallels between their feelings of social isolation and those expressed by the characters in the films who felt ‘trapped’ in their own bodies as a result of their neurological conditions. Although watching movies cannot reproduce the experiential learning that occurs through direct interactions with human beings affected by neurological illness, we found ‘cinemeducation’ to be a particularly useful and enjoyable method for maintaining neurological education under the extenuating circumstances of the COVID-19 pandemic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.020 |
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".