Health seeking behaviours among electronic waste workers in Ghana
Bibliographic record
Abstract
BACKGROUND: Electronic waste workers are prone to various illnesses and injuries from numerous hazards thus the need for them to seek health care. The aim of this study was to describe health-seeking behavior, and social and other factors affecting this behavior, among electronic waste workers at Agbogbloshie, Accra, Ghana. METHODS: In-depth interviews were conducted and analyzed qualitatively from a grounded theory perspective. RESULTS: Workers experienced various kinds of ailments. These included physical injuries, chest and respiratory tract associated symptoms, malaria, headaches, body pains and stomach discomfort. They reported seeking health care from multiple sources, and the main determinants of health seeking behaviour were severity of illness, perceived benefit of treatment, accessibility of service, quality of service, ease of communication with service provider and cost of health care. CONCLUSION: Multiple sources of health care were used by the e-waste workers. As cost was a major barrier to accessing formal health care, most of the workers did not subscribe to health insurance. Since enrollment in health insurance is low amongst the workers, education campaigns on the need to register with the National Health Insurance Scheme would facilitate access to formal health care and could result in improved health outcomes among e-waste workers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".