Psychosocial Factors Issues in Construction Workers: A Systematic Review and Future Research Directions
Bibliographic record
Abstract
The systematic literature review several previous studies indicates a growing focus on psychosocial factors issues in construction workers' research in recent years, which is predicted to continue.However, currently, there is no comprehensive framework with clearly defined indicators or dimensions to analyze the role of psychosocial factors in the context of construction workers.This paper aims to provide in-depth insights into and analyze psychosocial factors issues in construction workers, identify about anything research topics that have been handled, and identify opportunities regarding other topics that can be carried out in future research.This paper applies a systematic literature review methodology design.Present a structured overview derived from 32 reputable international journal articles indexed in the Scopus database.The results are based on selected reputable international journal articles are clustered based on the analytical framework containing eight psychosocial factors in construction worker research, namely demand, control, support, stress, condition, satisfaction, description, and conflict.In addition, this paper finds four theoretical frameworks based on previous research, namely JD-R model, JD-C model, P-E fit theory, and TPB model that can be applied.This literature study will contribute to analyzing the influence of psychosocial factors in the context of construction workers in presenting a framework of indicators or dimensions that can be applied.Know the theoretical framework and methodology that has been used.Identify research topics, areas, and additional opportunities to link other variables in future research.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".