Evaluating the implementation of virtual Goal Management Training among Veterans with posttraumatic stress disorder
Bibliographic record
Abstract
Introduction: Posttraumatic stress disorder (PTSD) is associated with changes in cognitive functioning across multiple domains that negatively affect Veterans' ability to engage in functional activities. Goal Management Training (GMT), a cognitive remediation strategy that aims to improve cognitive functioning of people with several neurological and neuropsychiatric conditions, was adapted to an online telemedicine format for use in the treatment of Veteran clients of an operational stress injury (OSI) clinic in Québec City, Québec, Canada, during the COVID-19 pandemic. The aim of this study was to evaluate the implementation of online GMT to determine its feasibility and effectiveness with OSI clients. Methods: The authors conducted and thematically analyzed two structured virtual focus groups with 11 OSI clinicians. Focus groups were recorded, and transcripts were thematically analyzed. Results: Clinicians reported that the cognitive strategies provided by GMT helped to improve functioning among some Veterans referred to the groups. Participation in GMT was also felt to have an overall positive effect on participants' affect and morale. Adaptations to fit both the online format and the needs of Veteran participants appeared key to maximizing effectiveness of the GMT program. Discussion: Adapting GMT to an online setting may assist in expanding accessibility of this cognitive remediation program to Veterans who could not otherwise benefit from this therapy in person. Moving forward, OSI clinics may want to consider implementing both in-person and online GMT groups to expand GMT uptake and improve clinical 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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".