Reducing serious injuries and fatalities in industrial construction-application of machine learning to analyze emotional intelligence and psychosocial factors
Bibliographic record
Abstract
Human and organizational factors are recognized as central in incidents; however, there has been little interconnection between individual and organizational psychological variables, such as interpersonal skills. Therefore, this study aims to glean theoretical and empirical insights to reduce severe injuries and fatalities and enhance safety performance. This study classified incidents of an industrial construction organization in Canada based on two attributes of interpersonal skills – emotional intelligence (EI) and psychosocial (PS) factors. A qualitative analysis using NVivo software was employed to classify 1000 incidents from 2018 to 2020 into EI factors. Since PS factors were not observed in the dataset, the analysis was extended to identify PS factors using machine learning techniques as a quantitative approach to analyze 45,603 incidents from 2014 to 2023. The classification was performed using keyword analysis of the incident descriptions. Further, co-occurrence networks were used to investigate patterns and validate the study results. The findings indicate that lack of self-awareness (domain of EI) (56.8%) and improper communication (domain of PS factor) (32.4%) were the most influential causes of incidents substantiated by the co-occurrence networks results. The study’s findings provide insights for decision-makers about the strategies needed to enhance safety performance in the industrial construction industry.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".