Utilization Based Technology Assessment and Evaluation of Cognitive Assessments for Canadian Armed Forces Members with Mild Traumatic Brain Injury
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».