An exploratory review of resiliency assessments after brain injury
Bibliographic record
Abstract
BACKGROUND: People with brain injury can have lower resiliency compared to the general public. Yet, resiliency facilitates positive processes to negotiate adversity after brain injury. Therefore, measuring resiliency after a brain injury is important. OBJECTIVE: The review aimed to (1) identify self-report resiliency outcome measures for use with people after acquired brain injury, using the process-based Traumatic Brain Injury Resiliency Model as the guiding conceptual framework, and (2) summarize the psychometric properties of the identified outcome measures and the utility of these measures in clinical rehabilitation. METHOD: The COSMIN guidelines for systematic reviews were followed to ensure appropriate reporting for each measure. Databases CINAHL, EMBASE, Medline, and PsychINFO were searched and independently reviewed by two people. Articles providing data on psychometric properties for measures of resilience for people with brain injury were retrieved. Psychometric properties and clinical utility (number of items, scoring details) were summarized. RESULTS: Thirty-two articles were retrieved, including nine measures of resiliency: Acceptance and Action Questionnaire-Acquired Brain Injury, Confidence after Stroke Measure, Connor-Davidson Resilience Scale, Daily Living Self-Efficacy Scale, General Self-Efficacy Scale, Participation Strategies Self-Efficacy Scale, Resilience Scale, Robson Self-Esteem Scale, and the Stroke Self-Efficacy Questionnaire. All measures have acceptable to excellent psychometric properties in accordance with the COSMIN guidelines. CONCLUSION: There are established measures of resiliency in brain injury rehabilitation. Future work may explore use of these measures in a clinical context and implementation of rehabilitation goals for improving resiliency after brain injury.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.033 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".