Selected indicators and determinants of women’s health in the vicinity of a copper mine development in northwestern Zambia
Bibliographic record
Abstract
BACKGROUND: Large projects in the extractive industry sector can affect people's health and wellbeing. In low- and middle-income countries (LMICs), women's health is of particular concern in such contexts due to potential educational and economic disadvantages, vulnerability to transactional sex and unsafe sex practices. At the same time, community health interventions and development initiatives present opportunities for women's and maternal health. METHODS: Within the frame of the health impact assessment (HIA) of the Trident copper mining project in Zambia, two health surveys were conducted (baseline in 2011 and follow-up in 2015) in order to monitor health and health-related indicators. Emphasis was placed on women residing in the mining area and, for comparison, in settings not impacted by the project. RESULTS: All measured indicators improved over time, regardless of whether communities were affected by the project or not. Additionally, the percentage of mothers giving birth in a health facility, the percentage of women who acknowledge that HIV cannot be transmitted by witchcraft or other supernatural means and the percentage of women having ever tested for HIV showed a significant increase in the impacted sites but not in the comparison communities. In 2015, better health, behavioural and knowledge outcomes in women were associated with employment by the project (or a sub-contractor thereof), migration background, increased wealth and higher educational attainment. CONCLUSIONS: Our study reveals that natural resource development projects can positively impact women's health, particularly if health risks are adequately anticipated and managed. Hence, the conduct of a comprehensive HIA should be a requirement at the feasibility stage of any large infrastructure project, particularly in LMICs. Continued monitoring of health outcomes and wider determinants of health after the initial assessment is crucial to judge the project's influence on health and for reducing inequalities over time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".