The effect of posture category salience on decision times and errors when using observation-based posture assessment methods
Bibliographic record
Abstract
Observation-based posture assessment methods (e.g. RULA, 3DMatch) require classification of body postures into categories. This study investigated the effect of improving posture category salience (adding borders, shading and colour to the posture categories) on posture selection error rates and decision times of novice analysts. Ninety university students with normal or corrected normal visual acuity and who were not colourblind, were instructed to select posture categories as quickly and accurately as possible, in five salience conditions (Plain (no border, no shading, no colour); Grey Border; Red Border; Grey Shading (GS) and Red Shading (RS)) for images presented in randomised blocks (240 classifications made by each participant) on a computer interface. Participants responded quickest in the Border conditions, classifying postures about 5% faster than in the Plain condition. Coloured diagrams significantly reduced posture classification errors by approximately 1.5%. Overall, the best performance, based on both error rate and decision time combined, resulted from incorporating a Grey Border to the posture category diagrams; a simple enhancement that could be made to most current observation-based posture assessment tools. PRACTITIONER SUMMARY: The salience of posture diagrams used in observation-based posture assessment tools was evaluated with respect to analyst error rates and decision times. The best performance resulted from incorporating a grey border to the posture diagrams; a simple enhancement that can be made to most current observation-based posture assessment tools.
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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 | 0.007 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".