Conditions that influence the elimination of postural constraints after office employees working with VDU have received ergonomics training
Bibliographic record
Abstract
OBJECTIVE: The goal of this article is to better understand how preventive measures are undertaken after training. It examines how certain variables, such as musculoskeletal pain, participant age and workstation and work content characteristics influence the reduction of postural constraints after office employees working with a computer have received ergonomics training. METHODS: A pre-test/post-test design was used. The 207 female office workers were given 6 hours of ergonomics training. The variables were determined using a self-administered questionnaire and an observation grid filled out 2 weeks before and 6 months after the training session. The FAC and HAC were used in the data processing. RESULTS: The presence or absence of musculoskeletal pain had no statistically significant influence on whether or not postural constraints were eliminated. The age of the participants and the possibility of adjusting the workstation characteristics and work content produced differentiated results with regard to postural constraint reduction. We concluded that trained people succeed in taking relevant and effective measures to reduce the postural constraints found in VDUs. However other measures than work station adjustments lead to this prevention and such training must be strongly supported by the various hierarchical levels of an enterprise or an institution.
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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 | 0.000 | 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 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".