The Effect of Conflict and Displacement on the Health of Internally Displaced People: The Colombian Crisis
Bibliographic record
Abstract
ABSTRACTDecades of violence in Colombia led to the relocation of millions of rural families into urban areas, where the violation of human rights and abuse add to the struggle to meet most basic needs. Lack of housing, economic instability, poor access to healthcare and education impact their overall health. Unfortunately, implementation of laws created to alleviate the crisis has been unsuccessful, as armed groups target aid efforts through violence and threats. While the role of physicians is limited, advocacy and collaboration with other organizations can help improve the health and well-being of this population. RÉSUMÉDes décennies de violence en Colombie ont mené au relogement de millions de familles rurales vers des milieux urbains, où la violation des droits de la personne et les sévices exacerbent la lutte pour subvenir aux besoins fondamentaux. Le manque de logement, l’instabilité économique, et l’accès restreint aux soins de santé et à l’éducation ont un impact sur leur santé globale. Malheureusement, l’établissement de lois conçues pour alléger la crise s’est révélé sans succès, puisque des groupes armés ciblent ces efforts d’aide humanitaire avec de la violence et des menaces. Bien que le rôle des médecins soit limité, les activités de plaidoyer et la collaboration avec d’autres organismes peuvent aider à améliorer la santé et le bien-être de cette population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".