Analyzing the Eye Gaze Behaviour of Students and Experienced Physiotherapists during Observational Movement Analysis
Bibliographic record
Abstract
Purpose: Physiotherapists use observational movement analysis (OMA) to inform clinical reasoning. This study aimed to (1) determine the feasibility of characterizing eye gaze behaviour during OMA with eye-tracking technology, (2) characterize experienced neurological physiotherapists’ and physiotherapy students’ eye gaze behaviour during OMA, and (3) investigate differences in eye gaze behaviour during OMA between physiotherapy students and experienced physiotherapists. Method: Eight students and eight physiotherapists wore an eye-tracking device while watching a video of a person with a history of stroke and subsequent concussion perform sit to stand. Feasibility criteria were (1) successful calibration of the eye tracker, and successful collection of data, for 80% of the participants and (2) moderate interrater reliability of the investigators, measured by intra-class correlation coefficients (ICCs). Three investigators independently recorded the participants’ foveal fixations. Differences between physiotherapists and students in number of fixations, duration per fixation, and total duration of fixations were evaluated using unpaired t-tests, mean differences, and 95% CIs. Results: Data were collected for all participants. ICCs ranged from 0.64 to 0.78. Fixations by physiotherapists were shorter (mean 368.5 [SD 80.8] ms) and greater in number (mean 18.9 [SD 2.2]) than those by students (mean 459.0 [SD 64.2] ms, p = 0.03, and mean 15.9 [SD 2.7], p = 0.03), respectively. Conclusions: Measuring eye gaze behaviour during OMA using eye tracker technology is feasible. Physiotherapists made more fixations of shorter duration than students. Further investigation of how experienced therapists perform OMA and apply it to clinical reasoning may inform the instruction of OMA.
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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 | 0.023 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".