The Design and Testing of Interactive Hospital Spaces to Meet the Needs of Waiting Children
Bibliographic record
Abstract
OBJECTIVE: To design an innovative interactive media display in a pediatric hospital clinic waiting space that addresses the growing demand for accessible, contact-surface-free options for play. BACKGROUND: In healthcare settings, waiting can be anxiety provoking for children and their accompanying family members. Opportunities for positive distraction have been shown to reduce waiting anxiety, leading to positive health outcomes. METHODS: An interactive media display, ScreenPlay, was created and evaluated using a participatory design approach and a combination of techniques including quality function deployment and mixed data elicitation methods (questionnaires, focus groups, and observations). The user and organizational design requirements were established and used to review contemporary strategies for positive distraction in healthcare waiting spaces and to conceptualize and test ScreenPlay. Ten staff members, 11 children/youths, and 6 parents participated in the design and evaluation of ScreenPlay. RESULTS: ScreenPlay provided a positive, engaging experience without the use of contact surfaces through which infections can be spread. It was accessible to children, youth, and adults of all motor abilities. All participants strongly agreed that the interactive media display would improve the healthcare waiting experience. CONCLUSIONS: ScreenPlay is an interactive display that is the result of a successful model for the design of healthcare waiting spaces that is collaborative, interdisciplinary, and responsive to the needs of its community. KEYWORDS: Design process, healing environments, hospital, interdisciplinary, pediatric.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".