Ergonomic support and workstation satisfaction among teleworkers in small and medium enterprises
Bibliographic record
Abstract
Background: Ergonomics can be adapted to the work-from-home context, e.g., training on adjustment, adjusted furniture provision, and professional evaluation of the ergonomic adjustment of one's workspace. However, very little research has examined the extent to which such supports have been offered to the diverse small and medium enterprise (SME) teleworkers population post-pandemic. Objectives: 1) Examine if, and how, i) ergonomics support provided to SME teleworkers, and ii) their satisfaction with the ergonomic adjustment of their workstation vary according to individual and organizational characteristics; 2) investigate the associations between provided ergonomics support and workstation satisfaction. Methods: 1162 teleworkers (616 supervisors) employed by SMEs in Canada completed an online questionnaire that included items regarding three ergonomic supports (i.e., training, equipment, evaluation) and workstation satisfaction. Results: Logistic and linear regression analyses indicated that several individual and organizational characteristics significantly (ps ≤ .05) predicted whether teleworkers received each form of ergonomic support (training: gender, enterprise size, union presence; equipment: supervisory role; evaluation: daily time at the computer, supervisory role) or their level of satisfaction with their workstation (union presence). Hours per week employers expected employees to telework and age significantly predicted being provided with ergonomic support (of any form) and workstation satisfaction. Linear regressions indicated that each ergonomic support was significantly related to increased workstation satisfaction. Conclusions: Some groups of teleworkers (e.g., women, younger workers, those in medium-sized enterprises, workers represented by a union) in SMEs seem more likely to receive ergonomic support than others. Receiving more ergonomic support cascades to more satisfaction with one's workstation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".