Age, human performance, and physical employment standards
Bibliographic record
Abstract
The proportion of older workers has increased substantially in recent years, with over 25% of the Canadian labour force aged ≥55 years. Along with chronological age comes age-related declines in functional capacity associated with impairments to the cardiorespiratory and muscular systems. As a result, older workers are reported to exhibit reductions in work output and in the ability to perform and/or sustain the required effort when performing work tasks. However, research has presented some conflicting views on the consequences of aging in the workforce, as physically demanding occupations can be associated with improved or maintained physical function. Furthermore, the current methods for evaluating physical function in older workers often lack specificity and relevance to the actual work tasks, leading to an underestimation of physical capacity in the older worker. Nevertheless, industry often lacks the appropriate information and/or tools to accommodate the aging workforce, particularly in the context of physical employment standards. Ultimately, if appropriate workplace strategies and work performance standards are adopted to optimize the strengths and protect against the vulnerability of the aging workers, they can perform as effectively as their younger counterparts. Our aim in this review is to evaluate the impact of different individual (including physiological decline, chronic disease, lifestyle, and physical activity) and occupational (including shift work, sleep deprivation, and cold/heat exposure) factors on the physical decline of older workers, and therefore the risk of work-related injuries or illness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".