A Pedagogic Strategy for Instructors of Post-secondary Sector Students Returning to Learn from Concussion
Bibliographic record
Abstract
Concussion is a functional brain injury that can produce physical, cognitive, emotional and sleep-related symptoms. Return to learn protocols designed to help students recovering from concussion recommend a gradual, symptom-governed, increase in cognitive activity before a return to full-time school attendance and participation. Return to learn in a post-secondary setting often means that instructors are tasked with accommodating for these students, some of whom are back in the classroom even though they are still experiencing symptoms. This paper presents a progressive, ramping approach to increasing cognitive load by using literal, interpretive and applied adaptations to already existing course requirements, with the goal of minimizing the risk of provoking or worsening post-concussion symptoms. Les commotions cérébrales sont des lésions fonctionnelles au cerveau qui peuvent provoquer des symptômes physiques, cognitifs et émotionnels ainsi que des troubles du sommeil. Selon le protocole de reprise des études conçu pour aider les étudiants qui se remettent d’une commotion, on recommande une augmentation graduelle de l’activité cognitive, en fonction des symptômes, avant d’effectuer un retour aux études à temps plein. Dans les établissements postsecondaires, on demande souvent aux instructeurs de mettre en place des mesures d’adaptation pour ces étudiants qui, dans certains cas, éprouvent encore des symptômes lorsqu’ils retournent en classe. Cet article présente une approche progressive visant à augmenter petit à petit la charge cognitive au moyen d’aménagements – d’ordre littéral, interprétatif et appliqué – apportés aux exigences de cours existantes, de manière à minimiser le risque de déclencher ou d’aggraver le syndrome post-commotion cérébrale.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".