Development and validation of a cognitive model-based novel questionnaire for measuring potential unsafe behaviors of construction workers
Bibliographic record
Abstract
The accident death rate in the construction industry is one of the highest among all occupational accidents. In order to identify the most common and direct causes of accidents, the unsafe behaviors of construction workers must be investigated, which necessities a questionnaire. Considering that safety climate research and behavior safety research barely explained the causes of unsafe behavior, this research was conducted from a cognitive model-based perspective. A new questionnaire was designed to evaluate the potentially unsafe behaviors, and a cognitive model with 11 factors was adopted. After verification by exploratory factor analysis, confirmatory factor analysis and reliability analysis, the new questionnaire showed good validity (content validity index < 0.79 and content validity ratio aaaa 0.42, average variance extracted > 0.5) and reliability (Cronbach’s α > 0.7, composite reliability > 0.7), and the cognitive model fitted well. Therefore, the new questionnaire is effective and reliable in assessing the causes of unsafe behaviors of construction workers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".