Assessing occupational hazards in the charcoal industry: a systematic review of exposure measurements, health outcomes and control measures v1
Bibliographic record
Abstract
Background: The charcoal industry employs large numbers of workers globally, particularly in low- and middle-income countries, and combines multiple physical, chemical, and thermal workplace hazards. Evidence about exposure levels, health outcomes, and effective control measures is fragmented across small field studies, program reports, and a limited epidemiological literature. A systematic synthesis is needed to clarify real-world risks and identify evidence gaps to inform occupational surveillance and prevention. Objective: To systematically identify, appraise, and synthesize published and grey-literature evidence on occupational hazards in the charcoal industry, specifically: (1) which exposures occur and the magnitudes reported; (2) which health outcomes are associated with charcoal work; and (3) what workplace controls or interventions have been described and evaluated. Methods: We will conduct comprehensive searches of MEDLINE/PubMed, Scopus, ProQuest, SpringerLink, Google Scholar, and Web of Science for studies published from 1 January 2000 to 9 November 2025 in English or Indonesian. Search strategies will combine controlled vocabulary (MeSH/EMTREE where available) and free-text terms for charcoal, occupational exposure, and relevant hazards/outcomes; reference lists and citation tracking will supplement database searches. Eligible studies include observational epidemiology (cross-sectional, cohort, case–control), exposure assessment studies, intervention/evaluation reports, relevant case reports/series, and qualitative studies; laboratory or animal studies without workplace data will be excluded. Records will be de-duplicated and screened in two stages by two independent reviewers; data extraction will be performed using a piloted form by two reviewers. Risk of bias will be assessed with design-appropriate tools (Newcastle–Ottawa/ROBINS-I, AXIS/JBI adaptations, RoB2 where applicable) plus a structured checklist for exposure-assessment quality. Data synthesis will be primarily narrative; where ≥2 studies report comparable quantitative outcomes (e.g., mean PM2.5, pooled effect estimates), random-effects meta-analysis will be considered. Heterogeneity will be assessed via I² and Cochran’s Q; publication bias will be assessed where appropriate. Planned subgroup and sensitivity analyses include region, worker subgroup, exposure metric (personal vs. area), study design, and informal vs. formal production. Certainty of evidence will be appraised using GRADE (adapted for observational evidence). Dissemination: Results will be reported following PRISMA, submitted for peer review, and shared with occupational health stakeholders and NGOs active in biomass and rural industries.
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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 | 0.029 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".