Bibliographic record
Abstract
PURPOSE: Disability due to work injury, trauma or disease is prevalent in our society. Also, due to slower growth of population and rapid increase in ageing population there may be an increasing pressure on shrinking labour pool. The purpose of this study is to review disability profile with common disabilities, socioeconomic impact of these disabilities, make a case for ergonomics as an enabler, and provide a couple of case studies to illustrate the point. METHOD: Major disability statistical records were reviewed to reveal rates of disability in some countries of the world. Among them the age and gender association of disability has been described. Furthermore the major categories of disabilities associated with systemic disorders and their gradations have been described, e.g. cardiovascular conditions, pulmonary conditions and joint diseases. Finally, using a feed forward mechanism a specific and customized ergonomic intervention was designed for two workers with knee injury. RESULTS: The prevalence of disability has been reported to range between 0.2% -20.9%. A significant association of disability with ageing was reconfirmed. A significant socio-economic impact of disability including differential employment rate for normal and disabled was discovered. A strategy and a means to achieve increased functionality in people with disability using ergonomic intervention has been described. Using a custom designed shin pad for rehabilitation of two workers with injured knee who were also on compensation were successfully returned to work many weeks before they may have been able to resume their work. CONCLUSION: Using a functional classification and developing functional profiles of people with disability may allow ergonomists to develop generic as well as specific solutions to successfully intervene in many cases and improve their functional capacity.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".