Preliminary efficacy and predictors of response to a remotely-delivered symptom self-management program for persistent symptoms after concussion
Bibliographic record
Abstract
Background More than a quarter of adults with concussion endure prolonged symptoms of >3 months. We developed the Concussion Education Self-Management program to help people manage persisting symptoms. Here, we assess feasibility, preliminary efficacy, and correlates of response.Methods N = 80 adults participated in the program; ages ranged from 18 to 65 years and time post-injury ranged from 6 months to 18 years. Weekly sessions, delivered remotely and in groups, comprised education and strategies for management of cognitive, emotional, and physical symptoms. Primary outcome: Confidence to self-manage symptoms. Secondary outcomes: Quality of life; mood/anxiety/stress. Predictors of response: Self-reported cognitive, emotional and physical symptoms at intake.Results Pre- to post-program improvements were observed in confidence to self-manage, p < 0.03; quality of life, p < 0.001; depression, p < 0.001; anxiety, p < 0.001; and stress, p < 0.001. Considering confidence to self-manage, those with fewer cognitive and physical symptoms benefitted more (p’s < 0.0005 and p < 0.01, respectively).Discussion This program shows promise for improving self-management of prolonged symptoms. Those with high symptom burden may need extra sessions to benefit. This is a cost-effective and scalable program that can reach people regardless of geographic location or impediments to travel.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".