Assessing Neurobehavioral Alterations Among E-waste Recycling Workers in Hong Kong
Why this work is in the frame
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Bibliographic record
Abstract
Background: E-waste workers in Hong Kong are handling an unprecedented amount of e-waste, which contains various neurotoxic chemicals. However, no study has been conducted to evaluate the neurological health status of e-waste workers in Hong Kong. This study aimed to evaluate the prevalence of neurobehavioral alterations and to identify the vulnerable groups among Hong Kong e-waste workers. Methods: We recruited 109 Hong Kong e-waste workers from June 2021 to September 2022. Participants completed standard questionnaires and wore a GENEActiv accelerometer for seven days. Pittsburgh Sleep Quality Index and Questionnaire 16/18 (Q16/18) were used to assess subjective neurobehavioral alterations. The GENEActiv data generated objective sleep and circadian rhythm variables. Workers were grouped based on job designation and entity type according to the presumed hazardous level. Unconditional logistic regression models measured the associations of occupational characteristics with neurobehavioral alterations after adjusting for confounders. Results: While dismantlers/repairers and the workers in entities not funded by the government were more likely to suffer from neurotoxic symptoms in Q18 (adjusted odds ratio: 3.18 [1.18-9.39] and 2.77 [1.10-7.46], respectively), the workers from self-sustained recycling facilities also have poor performances in circadian rhythm. Results also showed that the dismantlers/repairers working in entities not funded by the government had the highest risk of neurotoxic symptoms compared to the lowest-risk group (i.e., workers in government-funded companies with other job designations). Conclusion: This timely and valuable study emphasizes the importance of improving the working conditions for high-risk e-waste workers, especially the dismantlers or repairers working in facilities not funded by the government.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it