Development and preliminary evaluation of the caregiver assistive technology outcome measure
Bibliographic record
Abstract
INTRODUCTION: Assistive technology is often recommended with the aim of increasing user independence and reducing the burden on informal caregivers. However, until now, there has been no tool to measure the outcomes of this process for caregivers. OBJECTIVES: To describe the development of the Caregiver Assistive Technology Outcome Measure (CATOM), a tool developed to measure the impact of assistive technology interventions on the burden experienced by informal caregivers, and to undertake preliminary evaluation of its psychometric properties. METHODS: Based on an existing conceptual framework, existing measures were reviewed to identify potential items in a preliminary version of the measure. Cognitive interviewing was used to identify items needing clarification. A revised CATOM and manual were then reviewed by clinicians. After revising some items based on the interview findings, the measure was piloted as part of an intervention study examining the impact of assistive technology on the users' informal caregivers (n = 44). RESULTS: Based on a review of 12 existing measures, a 3-part measure was developed and questions were refined based on cognitive interviews with informal caregivers and feedback experienced assistive technology practitioners. For the activity-specific and overall portions of the measure, the 6-week, test-retest intraclass correlations coefficients were 0.88 (95% CI 0.64-0.96) and 0.86 (95% CI 0.60-0.95), respectively. The CATOM data correlated as hypothesized with other measures. CONCLUSION: The CATOM is a promising measure with good content validity and encouraging psychometric properties.
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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 | 0.033 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".