A Comparison of Tablet Computer and Paper-Based Questionnaires in Healthy Aging Research
Bibliographic record
Abstract
BACKGROUND: Digital questionnaire delivery offers many advantages to investigators and participants alike; however, evidence supporting digital questionnaire delivery via touchscreen device in the older adult population is lacking. OBJECTIVE: The objective of this study was to compare the use of tablet computer-delivered and printed questionnaires as vehicles for the collection of psychosocial data from older adults to determine whether this digital platform would be readily adopted by the sample, and to identify whether tablet delivery influences the content of data received. METHODS: The participants completed three questionnaires using both delivery methods, followed by a brief evaluation. RESULTS: A nonparametric one-sample binomial test indicated a significantly greater proportion of individuals preferred the tablet-delivered questionnaires (z=4.96, SE 3.428, P<.001). Paired sample t tests and Wilcoxon sign-rank tests indicated that measures collected by each method were not significantly different (all P≥.273). Ease of use of the tablet interface and anxiety while completing the digital questionnaires were significantly correlated with preferences, (rs=.665, P<.001 and rs=.552, P<.001, respectively). Participants most frequently reported that the tablet delivery increased speed of use and improved data entry, although navigation was perceived as being more difficult. By comparison, participants felt that the paper packet was easier to read and navigate, but was slow and cumbersome, and they disliked the lack of dynamic features. CONCLUSIONS: This study provides preliminary evidence suggesting that questionnaires delivered to older adults using contemporary tablet computers may be acceptable and do not substantively influence the content of the collected data.
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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 | 0.036 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".