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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".