Utilization Based Technology Assessment and Evaluation of Cognitive Assessments for Canadian Armed Forces Members with Mild Traumatic Brain Injury
Bibliographic record
Abstract
Canadian Armed Forces service members (CAF-SMs) have an increased risk of sustaining mild traumatic brain injuries (mTBI; Garber, Rusu, & Zamorski, 2014). MTBI can result in reduced cognitive functioning which may lead to barriers to participation in everyday occupations of CAF-SMs. Military contexts necessitate high levels of cognitive functioning; compromising this can potentially result in decreased efficiency and effectiveness, along with an increased risk of harm to self, the unit, and mission (Radomski, Davidson, Voydetich, & Erickson, 2009). Assessing cognitive functioning is necessary to ensure that CAF-SMs can perform their military duties safely and proficiently. Interventions to improve cognitive functioning are most effective when a reliable, valid, specific, and function-based cognitive assessments are employed (Radomski, Davidson, Voydetich, & Erickson, 2009; Soble, Critchfield, & O’Rourke, 2016). Despite this, healthcare professionals commonly assess cognition utilizing dated assessments with varying levels of validity and reliability, and only measure specific domains of cognition (Larner, 2017). Neurocognitive computerized assessment tools (NCATs) are widely utilized in other global militaries and have multiple benefits including potentially increased inter- and intra-rater reliability, ease of administration, reduced time to administer, and ease of calculating and analysing results (Cernich, Brennana, Barker, & Bleiberg, 2007). Evidence-based research of cognitive assessments with the CAF context is required to increase the safety, productivity, and quality of life of those CAF-SMs affected by mTBI. Even when cognitive assessment tools that embrace technology are utilized, significant gaps in research and clinical knowledge remain. The overall purpose of this research is to investigate best practice approaches for the implementation of cognitive assessments for CAF-SMs who have sustained an mTBI. This will assist with advancing clinical practices within CFHS and improve healthcare services for this demographic. A pragmatic paradigm is the essence of this project and a mixed-methods research design will be employed throughout. By meeting the CAF organization at their point of current progress and aligning realistically with their current state of policy, procedure, and plans, a feasible implementation path will emerge leading to better healthcare for those CAF-SMs who experience cognitive dysfunction due to mTBI. The overall project will be guided by the Active Implementation Frameworks (AIFs; Fixsen, Naoom, Blase, Friedman, & Wallace, 2005) and Utilization-Focused Evaluation Framework (UFE; Patton, 2013). This PhD project consists of 4 sections which follow the stages of AIFs and UFE and mixed-method research design: 1. A Model for Neurofunctional Health: The Canadian Model of Cognitive Skills 2. Neurocognitive Assessment Tools for Military Personnel with Mild Traumatic Brain Injury: A Scoping Literature Review 3. Perceptions of Canadian Armed Forces Healthcare Professionals on Cognitive Assessment Processes within Canadian Armed Forces Health Services: A Mixed Methods Analysis 4. Technology Acceptance of the BrainFX® SCREEN amongst Canadian Armed Forces Members and Veterans with Combat Related Posttraumatic Stress Disorder: Pre/Post Analysis
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".