Psychometric properties of measures of motivation and engagement after acquired brain injury.
Bibliographic record
Abstract
OBJECTIVE: This study investigated psychometric properties of the Motivation for Traumatic Brain Injury Rehabilitation Questionnaire (MOT-Q), the Brain Injury Rehabilitation Trust Motivation Questionnaire-Self (BMQ-S), the Rehabilitation Therapy Engagement Scale-Revised (RTES-R), and the BMQ-Relative (BMQ-R) in individuals with an acquired brain injury (ABI). DESIGN: Thirty-nine patients with an ABI completed the MOT-Q, BMQ-S, measures of apathy (Apathy Evaluation Scale-Self), insight (Patient Competency Rating Scale-Self), depression, and anxiety (HADS). Twenty clinicians provided 39 ratings using the RTES-R, BMQ-R, measures of patient apathy (Apathy Evaluation Scale-Clinician) and insight (Patient Competency Rating Scale-Clinician). Internal consistency, test-retest reliability, interrater reliability, and convergent validity were estimated. RESULTS: The MOT-Q (α = .93) and BMQ-S (α = .91) had excellent internal consistency and test-retest reliability (intraclass correlation coefficient [ICC] = 0.80 and 0.85). The MOT-Q and BMQ-S did not correlate with each other. The MOT-Q correlated with insight (r = -0.37, p < 0.05). The BMQ-S correlated with insight (r = -0.44, p < 0.01), apathy (r = .50, p < 0.01), depression (r = .55, p < 0.01), and anxiety (r = .49, p < 0.01). The RTES-R (α = .96) and BMQ-R (α = .95) had excellent internal consistency and good interrater reliability (ICC = 0.67 and 0.68). The RTES-R and BMQ-R correlated with each other (r = -0.88, p < 0.01), with apathy (r = -0.82 and r = .88, p < 0.01), and insight (r = -0.61 and r = .63, p < 0.01). CONCLUSIONS: The MOT-Q, RTES-R, BMQ-S, and BMQ-R have good reliability and validity. Using the MOT-Q and BMQ-S together may provide additional insight. (PsycINFO Database Record
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".