Reducing Physical Risk Factors in Construction Work Through a Participatory Intervention: Protocol for a Mixed-Methods Process Evaluation
Bibliographic record
Abstract
BACKGROUND: Previous research has shown that reducing physical workload among workers in the construction industry is complicated. In order to address this issue, we developed a process evaluation in a formative mixed-methods design, drawing on existing knowledge of the potential barriers for implementation. OBJECTIVE: We present the design of a mixed-methods process evaluation of the organizational, social, and subjective practices that play roles in the intervention study, integrating technical measurements to detect excessive physical exertion measured with electromyography and accelerometers, video documentation of working tasks, and a 3-phased workshop program. METHODS: The evaluation is designed in an adapted process evaluation framework, addressing recruitment, reach, fidelity, satisfaction, intervention delivery, intervention received, and context of the intervention companies. Observational studies, interviews, and questionnaires among 80 construction workers organized in 20 work gangs, as well as health and safety staff, contribute to the creation of knowledge about these phenomena. RESULTS: At the time of publication, the process of participant recruitment is underway. CONCLUSIONS: Intervention studies are challenging to conduct and evaluate in the construction industry, often because of narrow time frames and ever-changing contexts. The mixed-methods design presents opportunities for obtaining detailed knowledge of the practices intra-acting with the intervention, while offering the opportunity to customize parts of the intervention.
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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 | 0.125 | 0.080 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".