Shedding light on autistic traits in struggling learners: A blind spot in medical education
Bibliographic record
Abstract
Some highly challenging, seemingly "unsolvable" situations that arise in medical education could be the result of autistic traits (AT) in learners. AT exist in physicians and learners, ranging from profiles compatible with DSM-5's criteria for autism spectrum disorder (ASD) to more subtle manifestations of ASD's "broader phenotype." Often associated with strengths and talents, AT may nonetheless pose significant challenges for learning, teaching, and practising medicine. Since AT remain widely under-recognized and misunderstood by educators, clinicians, and affected individuals alike, they represent a blind spot in medical education. The use of a "neurodiversity lens" to examine challenging situations may help educators consider different pedagogical approaches to address those potentially stemming from AT.This paper aims to raise awareness and understanding of AT-related difficulties in struggling medical learners. To overcome the blind spot challenge and help develop this "neurodiversity lens," we explore different angles. Beyond any diagnostic consideration, we offer a series of contextual examples, paralleled with explanatory concepts from the field of ASD. We also underline the role of context on functional impact and describe the often ill-defined pattern of challenges encountered, as well as the fertile grounds for interpersonal misunderstandings and disrespect. We propose historical, cultural, and clinical reasons likely contributing to the blind spot. Mindful of the potential risks of prejudice associated with identifying AT-related difficulties, we underline the necessity and feasibility of conciliating diversity and dignity with accountability standards for medical competence.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".