The effectiveness of the Interactive Metronome® as a tool to improve selective attention of veterans within their roles in post-secondary education settings in eastern North Carolina
Bibliographic record
Abstract
The purpose of this study was to measure the effectiveness of the Interactive Metronome specific to improving attention. As veteran enrollment continues to increase in higher education, attention in the classroom and workforce due factors such as Post Traumatic Stress Disorder (PTSD) and Traumatic Brain Injury (TBI) have become an issue. Could veterans who have expressed concern of attention problems benefit from the Interactive Metronome (IM)? Three veteran students and employees from East Carolina University self-identified as having problems with attention and participated in the study. Subjects were given the IM-Home system after meeting with the PI to complete the Canadian Occupational Performance Measure (COPM), d2 Test of Attention, and learning to use the system. After 15 at-home sessions (4-6 weeks) with the IM, participants retook the COPM and D2. It was found that all participants rated their satisfaction with attention in school or work higher than before they started the IM. Additionally, post-test scores of the d2 Test of Attention showed that all participants were able to process more information. Based on these results and past studies, it is believed that the Interactive Metronome is a valuable tool in the rehabilitation process and may be especially helpful for veterans with attention problems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".