Do People With Severe Traumatic Brain Injury Benefit From Making Errors? A Randomized Controlled Trial of Error-Based and Errorless Learning
Bibliographic record
Abstract
BACKGROUND: Errorless learning (ELL) and error-based learning (EBL) are commonly used approaches to rehabilitation for people with traumatic brain injury (TBI). However, it is unknown whether making errors is beneficial in the learning process to promote skills generalization after severe TBI. OBJECTIVE: To compare the efficacy of ELL and EBL for improving skills generalization, self-awareness, behavioral competency, and psychosocial functioning after severe TBI. METHOD: A total of 54 adults (79% male; mean age = 38.0 years, SD = 13.4) with severe TBI were randomly allocated to ELL or EBL and received 8 × 1.5-hour therapy sessions that involved meal preparation and other goal-directed activities. The primary outcome was total errors on the Cooking Task (near-transfer). Secondary outcome measures included the Zoo Map Test (far-transfer), Awareness Questionnaire, Patient Competency Rating Scale, Sydney Psychosocial Reintegration Scale, and Care and Needs Scale. RESULTS: Controlling for baseline performance and years of education, participants in the EBL group made significantly fewer errors at postintervention (mean = 36.25; 95% CI = 32.5-40.0) than ELL participants (mean = 42.57; 95% CI = 38.8-46.3). EBL participants also demonstrated greater self-awareness and behavioral competency at postintervention than ELL participants ( P < .05). There were no significant differences on other secondary outcomes ( P > .05), or at the 6-month follow-up assessment. CONCLUSION: EBL was found to be more effective than ELL for enhancing skills generalization on a task related to training and improving self-awareness and behavioral competency.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".