Educator perspectives on concussion management in the college classroom: a grounded theory introduction to collegiate return-to-learn
Bibliographic record
Abstract
OBJECTIVES: To gather the perspectives of collegiate instructors regarding how concussion is managed within the college classroom. To introduce the themes surrounding collegiate return-to-learn (RTL) and the classroom management of students with concussion. DESIGN: Qualitative grounded theory. SETTING: Large, public university in the Midwest. PARTICIPANTS: Twenty-three college instructors participated in a private, semistructured, audio-recorded, one-on-one interview. Participants included 12 males and 11 females. Interview recordings were transcribed verbatim, followed by an iterative process of open-coding and axial-coding, performed by two researchers. RESULTS: Three themes emerged from the coded data: (1) awareness-external knowledge of concussion and previous experiences, (2) legitimacy-medical note provided and no note provided and (3) accommodating the student-instructor's role and feasibility of the accommodation. Psychosocial factors such as small class sizes, graduate-level students and an instructor's empathy appeared to influence an instructor's decision making when accommodating a student recovering from concussion. CONCLUSION: These novel data provide foundational evidence regarding how college instructors perceive and subsequently manage concussion within the classroom, while also offering accuracy to aims of subsequent collegiate RTL investigations ARTICLE SUMMARY: RTL is an emerging field within concussion management, yet is grossly underexplored within the college setting. By utilising a grounded theory approach, this article introduces the themes that dictate the landscape of RTL for a college student.
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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 | 0.031 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".