Developing a heat stress model for construction workers
Bibliographic record
Abstract
Purpose Heat stress, having caused preventable and lamentable deaths, is hazardous to construction workers in the hot and humid summers of Hong Kong. The purpose of this paper is to develop a heat stress model, based on the Wet Bulb Globe Temperature (WBGT) index. Design/methodology/approach Field studies were conducted during the summer time in Hong Kong (July to September 2010). Based upon 281 sets of synchronized meteorological and physiological data collected from construction workers in four different construction sites between July and September 2010, physiological, work‐related, environmental and personal parameters were measured to construct and verify the heat stress model. Findings It is found that drinking habit, age and work duration are the top three significant predictors to determine construction workers' physiological responses. Other predictors include percentage of body fat, resting heart rate, air pollution index, WBGT, smoking habit, energy consumption, and respiratory exchange rate. The accuracy of the model is verified against data which have not been used in developing the model. The accuracy of the heat stress model is found to be statistically acceptable (Mean Absolute Percentage Error=5.6 percent, Theil's U inequality coefficients=0.003). Practical implications Based on these findings, appropriate work‐rest pattern can be designed to safeguard the well being of workers when working in a hot and humid environment. Originality/value The model reported in this paper provides a more scientific and reliable prediction of the reality which may benefit the industry to produce solid guidelines for working in hot weather.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".