Validation of the Framework for Assessing Occupational Health Risks of Municipal Solid Waste Handlers
Bibliographic record
Abstract
BACKGROUND: The occupational health risks associated with municipal solid waste handling are widely documented in literature. However, no framework has been developed for their assessment. The aim of this study was to develop and validate a tool for use by local government structures.METHODS: Epidemiological evidence on human health risks associated with municipal solid waste management (MSWM) was obtained from literature and primary data collected from the study sites. An analysis of strengths, weaknesses, opportunities and threats (SWOT) of available human and environmental risk assessment frameworks was done and the findings were used as a base for the framework. The proposed framework was validated through iteration workshops in small, medium and large local government structures. Also, it was presented in a safety and health conference, in order obtain the input of occupational health and safety practitioners, researchers and policy makers.RESULTS: A draft framework was produced, validated and revised to incorporate resolutions made from the iteration workshops. The final framework constitutes four inputs, six phases and four principles. Each phase has defined outputs.CONCLUSION: The applicability of the framework to situations of resource-constrained economies has been tested through validation workshops in small, medium and large local government structures of a low income country. In light of the multi-methods used in developing the framework and the input of practitioners in validation workshops, the framework appears relevant for the purposes of assessing occupational health risks of municipal solid waste handlers (MSWHs).
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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 | 0.091 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".