Development and Evaluation of Sleep Disorder Decision Aids for Veterans With Mild Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: First, to summarize the design of novel decision aid prototypes aimed at facilitating shared decision-making for Veterans with co-morbid mild traumatic brain injury (mTBI) and sleep disorders (insomnia, obstructive sleep apnea [OSA]) in the Veterans Health Administration (VHA) Polytrauma/TBI System of Care (PSC). Second, to elicit feedback regarding usability, acceptability, and feasibility of prototypes to inform future implementation. SETTING: Nationwide VHA PSC sites. PARTICIPANTS: Clinicians included VHA providers involved in the management of mTBI and/or sleep disorders in the VHA PSC ( n = 7). Veterans included those with a clinician-confirmed mTBI who received care for insomnia disorder and OSA within the past year ( n = 5). DESIGN: Convergent parallel mixed methods. MAIN MEASURES: Semi-structured interview guides; System Usability Scale; Ottawa Decision Aid Acceptability Scale. RESULTS: Participants found the decision aid prototypes easy to use, highlighting its accessibility and features enabling an easy comparison of treatments. However, participants recommended changes to simplify and improve the design. Decision aids were seen as acceptable, providing essential information for Veterans with mTBI and facilitating shared decision-making among providers, Veterans, and other decision partners (eg, spouse). Removal of non-essential content was recommended to increase acceptability. Decision aids were considered feasible to implement, though extending the decision-making process beyond the initial encounter and accounting for time constraints were recommended. CONCLUSIONS: Findings highlight that the decision aids are easy-to-use, feasible to implement, and capable of improving Veteran-centered management of sleep disorders among those with mTBI. Nonetheless, clinicians and Veterans offered recommendations for changes that can improve the utility of the decision aids and facilitate their seamless integration into routine care for Veterans with co-morbid mTBI and sleep disorders. Findings lay the foundation for efforts aimed at implementing the decision aids into routine care for sleep disorders in the VHA PSC, aligning care decisions with Veteran preferences and improving outcomes.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".