Evaluating Workers’ Well-being in Off-site Construction Facilities
Bibliographic record
Abstract
Well-being is defined as “the way people feel and function on a personal and social level and how they evaluate their lives as a whole”. It encompasses several interrelated dimensions, including the physical, emotional, social, financial, environmental, vocational, and intellectual. An individual’s well-being is influenced not only by their personal experiences but also by their experiences at the workplace. Individuals spend nearly one-third of their life at work and tend to carry their experiences into non-work-related domains. As a result, promoting a work environment centered on employees’ health, happiness, and satisfaction not only is important to ensure efficiency and productivity but, more importantly, represents a fundamental dimension of social responsibility and ethical obligation. In the context of off-site construction, the production facility is the primary workplace. Studies have shown how off-site construction can positively influence workers’ well-being. To aid in the realization of off-site construction’s full potential, this paper proposes a multi-step generic framework (Well-OS) to assess and evaluate well-being in off-site construction facilities. Well-OS comprises three phases: well-being factor identification, current-state assessment, and intervention design, implementation, and evaluation. A hypothetical case of off-site construction workers’ thermal comfort is presented to illustrate how the framework can be applied. Ultimately, the framework provides off-site construction managers with a structured approach for conducting a baseline analysis of well-being and proposes the necessary promotive and preventive interventions to improve workers’ well-being and productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".