Adolescent Reactions to Icon-Driven Response Modes in a Tablet-Based Health Screening Tool
Bibliographic record
Abstract
Increasingly popular touch-screen electronic tablets offer clinics a new medium for collecting adolescent health screening data in the waiting area before visits, but there has been limited evaluation of interactive response modes. This study investigated the clarity, comprehensibility, and utility of icon-driven and gestural response functions employed in one such screening tool, TickiT. We conducted cognitive processing interviews with 30 adolescents from Vancouver (aged 14-20 years, 60% female, 30% English as a second language) as they completed the TickiT survey. Participants used seven different interactive functions to respond to questions across 30 slides, while being prompted to articulate their thoughts and reactions. The audio-recorded, transcribed interviews were analyzed for evidence of comprehension, nuances in response choices, and youth interest in the modes. Participants were quite receptive to the icon response modes. Across demographics and cultural backgrounds, they indicated question prompts were clear, response choices appropriate, and response modes intuitive. Most said they found the format engaging and would be more inclined to fill out such a screening tool than a paper-and-pencil form in a clinical setting. Given the positive responses and ready understanding of these modes among youth, clinicians may want to consider interactive icon-driven approaches for screening.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".