Interventions to help adults with concussion return to work: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Clinicians require guidance to aid patients who are attempting to return to work (RTW) after concussion. This systematic review synthesizes the results of interventions to determine what types of interventions (ie, early education and intervention, psychological, rehabilitative, and interdisciplinary) are effective, what behavior change techniques (BCTs) are most frequently used, and which BCTs are most promising. LITERATURE SURVEY: MEDLINE ALL, PsycINFO, and CINAHL databases were searched for literature published January 2000 to the end of December 2023. METHODOLOGY: Peer-reviewed adult concussion intervention studies of any design with RTW measurements were included. A narrative review describes the effectiveness of intervention type. For BCTs, the frequency of BCTs used in experimental conditions is provided. In addition, promise ratios were calculated to establish which BCTs are most effective. SYNTHESIS: Psychotherapy, neuropsychological training, vestibular rehabilitation, vision rehabilitation, exercise, compensatory strategies, cognitive training, vocational support, and interpersonal training may be efficacious. The BCT information about health consequences was used frequently in experimental conditions. Effective BCTs provided instruction on how to perform the behavior, reduce negative emotions, and action planning; participants were also provided information about health consequences and encouraged to do problem solving. CONCLUSIONS: The promising interventions and BCTs identified in this article may be considered by qualified practitioners when helping patients with concussion RTW (PROSPERO: CRD42022384151).
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".