Bibliographic record
Abstract
This thesis explores the role of interactive tabletops in clinical consultations, aiming to improve patient outcomes through enhanced patient understanding, activation, decision-making, and satisfaction.This research assesses the efficacy of interactive tabletops against traditional consultation mediums like Personal Computer (PC) monitors and printouts of medical data.A high-fidelity prototype of an application for interactive tabletops, developed through iterative design, served as the basis for a within-subjects experiment comparison of these different mediums.This thesis's findings reveal that interactive tabletops boost patient understanding, activation, decision-making, and satisfaction.By demonstrating the positive impacts of interactive tabletops in clinical consultations, this thesis advocates for their broader adoption to create more patient-centred and data-informed consultation experiences.Additionally, we contributed a set of User Experience (UX) and User Interface (UI) design recommendations for interactive tabletops in clinical consultation settings.Additionally, I turn my acknowledgment inward.After enduring moments of doubt, shedding tears, and dedicating countless hours to writing, I commend myself for concluding this significant chapter.To finalize, I would like to dedicate this thesis to every international student who might feel overwhelmed during their initial months: trust in your resilience and the journey ahead.Believe in yourself, and you will find your footing and navigate the challenges gracefully.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".