Improving Older Adults' Experience with Interactive Voice Response Systems
Bibliographic record
Abstract
BACKGROUND: Interactive voice response (IVR) systems use computer-based voice recognition and software algorithms to conduct human/computer interactions. In recent years, there has been a proliferation of IVR applications in business and healthcare. The available evidence suggests that older people have negative attitudes towards IVR and experience significant difficulties using these systems. OBJECTIVE: The goal of this project was to identify areas of difficulties in IVR use by older people and propose strategies for improvement. MATERIALS AND METHODS: During two focus groups, we examined older people's perceptions of IVR systems and the most common difficulties experienced by seniors in interacting with these systems. We also recorded their suggestions for improvement of IVR. RESULTS: Frequency and chi square analyses were performed on the focus groups data. Some of the difficulties reported by participants in this study were congruent with previous findings, but we also uncovered some additional problems, such as frustration for not being able to reach an operator, being asked to wait too long on hold, being unable to recover from mistakes, and an absence of shortcuts in the systems. In addition, significant number of participants indicated that they prefer a system that adjusts to them automatically as opposed to a system that allows for adjustment. CONCLUSION: Generally, our findings suggest that the poor acceptability of IVR systems by older people could be improved by designing IVR algorithms that detect difficulties during an ongoing IVR exchange and direct people to different algorithms adapted for each person.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".