Psychometric properties of a Power Mobility Caregiver Assistive Technology Outcome Measure
Bibliographic record
Abstract
Caregiver burnout is a serious concern among informal caregivers, especially for those who provide care to individuals with more severe limitations such as power mobility users. The Power Wheelchair Caregiver Assistive Technology Outcome Measure tool measures device specific and overall burden experienced by informal caregivers of power mobility users. A one-month, test-retest study was conducted to examine the reliability, internal consistency, and construct validity of the Power Wheelchair Caregiver Assistive Technology Outcome Measure. Two construct validity measures were administered: the Hospital Anxiety and Depression Scale and the Late Life Disability Index. The test-retest-reliabilities of part 1 (power wheelchair specific burden) and part 2 (general caregiving burden) were 0.769 and 0.843 respectively. Scores on part 1 were moderately and positively correlated with part 2 and with frequency of participation. Scores on part 2 were moderately and negatively correlated with anxiety, depression, and positively with perceived limitation of participation. The strength and direction of these correlations provide support for the construct validity of the measure and suggest part 1 and part 2 provide complementary information. Further testing is needed to assess the clinical utility and responsiveness of the measure.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".