Conducting Eye‐Tracking Research in Acute Care: A Scoping Review of Ethical, Feasibility and Acceptability Challenges
Bibliographic record
Abstract
AIM: To identify and synthesise the ethical, feasibility and acceptability challenges associated with implementing eye-tracking research with clinicians in acute care settings and to explore strategies to address these concerns. DESIGN: Scoping review using the Joanna Briggs Institute methodology. DATA SOURCES: Six databases (MEDLINE, CINAHL, EMBASE, Web of Science, APA PsycInfo and ProQuest Dissertations & Theses Global) were searched for peer-reviewed articles. Reference lists of included studies were also hand-searched. METHODS: Eligible studies involved clinicians using or interacting with eye-tracking devices in acute care environments and addressed at least one ethical, feasibility, or acceptability consideration. Data were extracted and thematically analysed. Knowledge users, including clinicians, ethicists and a patient partner, were engaged during protocol development and findings synthesis. RESULTS: Twenty-five studies published from 2010 to 2024 were included. Seven challenges were identified: obtaining ethical approval, managing consent, privacy and confidentiality concerns, collecting data in unpredictable environments, interference with care, participant comfort and data loss or unreliability. Knowledge users highlighted the importance of early institutional engagement, clear protocols, continuous consent and context-sensitive ethical reflection. CONCLUSIONS: Eye-tracking offers valuable insights into clinician behaviour and cognition, but its implementation in acute care raises complex ethical and methodological issues. Responsible use requires anticipatory planning, stakeholder engagement and flexible yet rigorous protocols. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: By informing the development of ethically sound study protocols and consent practices, this work contributes to safer, more transparent and patient-centred research that respects participant autonomy and protects clinical workflows. REGISTRATION: The protocol was registered with the Open Science Framework (https://osf.io/jn4yx). REPORTING METHOD: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA; Page et al., 2021) and its Extension for Scoping Reviews (Tricco et al., 2018). PATIENT AND PUBLIC CONTRIBUTION: A patient partner was involved in protocol development, interpretation of findings and development of study recommendations. Their contributions included participating in advisory groups and providing feedback alongside clinicians and ethicists during focus groups. This input helped ensure the research addressed patient-relevant priorities and informed the development of ethically responsible practices for conducting eye-tracking research in clinical care settings.
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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 | 0.322 | 0.566 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".