The effect of posture category salience on decision times and errors when using observation-based posture assessment methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it