Predictors of Successful Communication With Interactive Voice Response Systems in Older People
Bibliographic record
Abstract
OBJECTIVES: Interactive voice response (IVR) systems are computer programs that can interact with people to provide a number of services from business to health care. However, surveys examining people's attitudes toward these systems have consistently found that people in general and older people in particular strongly dislike these systems. We wanted to determine the memory and cognitive abilities that predict successful IVR interactions for older people. METHOD: We compared the performance of 185 older adults (aged 65 and older) on normed cognitive tests (the Wechsler Adult Intelligence Scale fourth edition and the Wechsler Memory Scale fourth edition) with their performance on 4 real-life IVR systems that included fact-finding at governmental agencies and plane ticket reservation. RESULTS: The results indicated that adults aged 65 and older experience significant difficulties in interacting with IVR systems. A significant number of people (20.5%) could not complete any of the tasks. Participants who could not complete any task were older and had the lowest full-scale IQ. However, there was little difference between the age of participants who completed 1, 2, 3, or 4 tasks. Rather, auditory memory and working memory were the best overall predictors for success in IVR tasks. DISCUSSION: The impact of poorer auditory memory and working memory is compounded by programming practices that increase the demand on these abilities and create unnecessary difficulties. Successful use of IVR systems could eventually complement in person health services.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".