Medical students’ perceptions on preparedness and care delivery for patients with autism or intellectual disability
Bibliographic record
Abstract
Introduction: To provide competent care to patients with autism spectrum disorder (ASD) or intellectual developmental disorder (IDD), healthcare professionals must recognize the needs of neurodivergent populations and adapt their clinical approach. We assessed the perceived preparedness of medical students to adapt care delivery for patients with ASD/IDD, as well as their perceptions on neurodiversity education. Methods: We conducted a sequential explanatory mixed-methods study on undergraduate medical students at McGill University during the academic year 2020-2021. We administered an online survey, followed by semi-structured interviews. We analyzed data using descriptive statistics and thematic analysis. We integrated findings at the interpretation level. Results: We included two-hundred-ten survey responses (~29% of class), and 12 interviews. Few students felt prepared to adjust care for patients with ASD/IDD despite most indicating doing so was important. Ninety-seven percent desired more training regarding care accommodation for neurodivergent patients. Thematic analysis unveiled the perception of current insufficient education, and the value of experiential learning. Discussion/Conclusions: This study highlights low perceived preparedness of medical students to accommodate care for neurodivergent patients, and a desire for more instruction. Incorporating interactive training in medical school curricula regarding modifying care delivery for neurodivergent individuals may improve the perceived preparedness of medical trainees to work with these patients and care quality.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".