Challenges of Building Health Impact Assessment Capacity in Developing Countries: a Review
Bibliographic record
Abstract
The published and grey literatures, including online technical reports and guidelines, about Health Impact Assessment (HIA) capacity building and training are reviewed. The review aims to compare country-specific HIA environments and different training materials and to identify appropriate training material for HIA in low- and middle-income (LMIC) settings, such as Mongolia. The few publications about HIA and capacity building found in scientific databases either describe the potential benefits of HIA training or discuss methodological issues. There is, however, a large body of grey literature, mostly institutional, available online. In assessing the HIA training literature, three key points arise: knowing the audiences’ roles when determining training design and content, being culturally sensitive and recognizing traditional knowledge in training and promoting elements of “system-wide capacity building” for HIA. There remains a need to increase the available literature and web content on HIA training and capacity building specifically designed for LMICs. Decisions will have to be made about what to translate and how to translate training materials into languages other than English.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".