Resilience-oriented intervention for war veterans with traumatic brain injury in remote period: study protocol and empirical evaluation of methodology
Bibliographic record
Abstract
Much attention is paid worldwide to the development of interventions that affect veterans' resilience to reduce post-concussion and post-traumatic symptoms. However, today there are a few of them and they have insufficient evidence base for effectiveness in improving the mental state of veterans. This article presents a protocol and results of empirical evaluation of methodology of research aimed at improving the effectiveness of rehabilitation of war veterans with traumatic brain injury in remote period, based on the study of resilience cognitive and emotional components and its recovery by improving the complex of psychocorrection and prognosis. We plan to conduct the study on 2019-2023 with participation a total of 140 demobilized combatants in the ATO/OUF zone. For psychological assessment we plan to use the scale of neurobehavioral symptoms, the posttraumatic stress disorder checklist 5, hospital anxiety and depression scale, Montreal cognitive assessment scale, Chaban quality of life scale, Connor-Davidson resilience scale. To study the variability of dependent variables under the influence of psychocorrection, we plan to use analysis of variance. To study the prognostic value of changes in the cognitive and emotional components of resilience during the process of rehabilitation we plan to use a regression analysis. Based on the results of an empirical assessment, the selected methods make it possible to obtain a detailed characteristic of the resilience of war veterans with traumatic brain injury in remote period, to evaluate the effectiveness of the psychocorrection program and the prognostic value of changes in the cognitive and emotional components of resilience. Taking into account the methodology empirical assessment results, it can be expected that the main group and the comparison group socio-demographic and clinical indicators will be equivalent to each other, which will make it possible to assert their homogeneity and use for comparison. Methods of statistical processing of the data obtained correspond to the nature of the statistical data, make it possible to systematize the data, establish the degree of reliability and confirm the results obtained.
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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 | 0.037 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".