Optimizing early education provided at the Hull-Ellis Concussion and Research Clinic: A multiple methods evaluation from the Toronto Concussion Study
Bibliographic record
Abstract
Objectives The purpose of this study was to determine factors and characteristics associated with changes in knowledge among adults receiving education within the first 8 weeks post-concussion. The study also aimed to understand desired preferences (i.e. content, format) for education post-concussion from the perspective of patients and physicians.Methods Patient-participants (17–85 years) were prospectively recruited within one week of a concussion. Participants received education over visits from Weeks 1 to 8 post-injury. Primary outcome measures were participant responses on a concussion knowledge questionnaire at Weeks 1 (n = 334) and 8 (n = 195), and feedback regarding education through interviews. Other variables collected included preexisting medical history, physician assessed recovery and symptoms.Results There was a significant increase in average knowledge on the concussion knowledge questionnaire across time (71% vs 75% correct; p = 0.004). Participants with higher levels of education, female sex and preexisting diagnoses of depression or anxiety had more correct responses at Week 1. Healthcare providers had varying comfort levels addressing mood-related symptoms.Conclusions There is a need to tailor education provided to concussion patients based on preinjury characteristics, i.e., mood disorders and demographic factors. Healthcare providers may need additional training in addressing mood symptoms and should modify the approach to fit patients’ unique needs.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".