New Modes of Operating for Construction Organizations during the COVID-19 Pandemic: Challenges, Actions, and Future Best Practices
Bibliographic record
Abstract
Construction organizations have been implementing different actions to control and mitigate effects of the COVID-19 pandemic on their workers and operations. Although some of these actions allowed construction organizations to remain productive during the pandemic, many organizations still struggle to cope with these effects. The construction industry has a need to identify the most effective actions that construction organizations can take to effectively control and mitigate the challenges created by the COVID-19 pandemic. This paper presents results of two surveys conducted with construction organizations, primarily in North America, and identifies the most effective mitigation actions to help construction organizations operate during this pandemic and develop evidence-based operational strategies to use during the current pandemic and any future pandemics. The contributions of this paper are (1) identifying an extensive list of possible actions to control and mitigate effects of the COVID-19 pandemic on construction organizations, (2) providing a categorization and methodology for assessing and ranking these actions, (3) identifying the most effective mitigation actions for construction organizations during the current COVID-19 pandemic and future pandemics, and (4) developing a comparative analysis of action prioritization for different stages of the COVID-19 pandemic to provide insight into the management of the adverse effects of pandemics on construction organizations. Data analysis of the survey results showed that construction organizations have been greatly affected by the COVID-19 pandemic in terms of their operational capacity, productivity, and workforce practices, and many organizations expect to have higher percentages of employees working remotely postpandemic they did prepandemic. Comparative analysis also showed an increasing trend in the importance of using technology to control and mitigate effects of the COVID-19 pandemic in construction organizations.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".