Older People's Attitudes Toward Interactive Voice Response Systems
Bibliographic record
Abstract
BACKGROUND: Interactive voice response (IVR) systems are computer programs that interact with people to provide a number of services from business to healthcare. The healthcare applications are particularly relevant to older adults because they are important consumers of medical services. However, research has found that older adults can experience significant difficulties with IVR and have more negative attitudes toward the technology. SUBJECTS AND METHODS: Seniors' attitudes appear to be related to their most recent experiences with IVR systems. The objective of this study was to examine attitudes toward four commercial or governmental IVR systems and how these attitudes relate to participants' ability to interact with the technology in a sample of 185 community-dwelling older (>65-year-old) adults. We also examined the effects of several demographic factors on both success and attitudes toward automated systems. RESULTS: We found a significant positive correlation between IVR success and attitudes toward IVR. However, a large subset of our sample gave high ratings despite experiencing significant difficulties with the systems. These participants tended to have lower full scale IQ. No gender differences emerged in terms of attitudes and ability to interact with IVR systems. CONCLUSIONS: Results also indicated that older adults in our sample viewed the IVR interaction as particularly demanding on attention and concentration abilities.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".