Development of the Computer and Technology Attitude Questionnaire (CaTAQ) to inform performance on computerised cognitive testing in older adults in the CogSCAN Study
Bibliographic record
Abstract
Abstract Background Computer‐administered neuropsychological assessment batteries (CNAs) have the potential to allow large‐scale cognitive screening and monitoring, increasing older adults’ access to cognitive assessment, and earlier diagnosis of and intervention for cognitive impairment. There is, however, little research on whether experience with and attitudes to computers and technology affect the validity, reliability and acceptability of CNAs in older adults. Here, we report the development and validation of an instrument to measure computer attitudes and computer experience in an older adult cohort from the CogSCAN study. Method The Computer and Technology Attitude Questionnaire (CaTAQ) is a new 27‐item self‐report questionnaire comprising five subscales (computer anxiety, comfort with computers, positive and negative attitudes to computers and computer learning self‐efficacy) that have been individually validated in diverse populations (young, predominantly Hispanic Los Angeles adults; Canadian business employees; older Pennsylvanian Caucasian adults), as well as items measuring experience with computers and related technologies. Responses from 196 community‐living older adults without dementia in Sydney, Australia (67% female, mean age 72.1 years, range 60‐91 years, mean years’ education 14.7, limited/no computer experience 9.2%) have been analysed to date. Result Confirmatory factor analysis supported a three‐factor model (anxiety/discomfort, positive about technology, negative about technology) with good fit to items from the first four subscales (Figure 1), and four computer learning efficacy items formed a single factor with good fit in a subsample of 79 participants who reported not knowing how to use a computer (Figure 2). Our measurement models for this older Australian sample therefore replicate previous validation of these subscales. Analyses in progress will further clarify the factor structure of the questionnaire and remove redundant items. Conclusion Results suggest the CaTAQ can provide nuanced information about older adults’ attitudes to computers and technology that have the potential to influence CNA performance and acceptability. The results will need to be confirmed in a less‐educated sample with less computer experience, and in people with Mild Cognitive Impairment or mild dementia. This new instrument has the potential to inform decisions about suitability of computerised cognitive testing for older adults in research and clinical settings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".