Managing tuberculosis among labor migrants: exploring alternative organizational approach
Bibliographic record
Abstract
Purpose High volume of international migration calls for the establishment of financial and organizational mechanisms that would ensure provision of treatment for tuberculosis (TB) among migrants. In the case of countries like Russia where budget funding goes for TB treatment, the need is acute as delivering these services is affected by social perception that they should be provided to taxpayers only. While official policies in Russia promote voluntary medical insurance as a way to cover their health care needs, the problem is that neither voluntary medical insurance, nor the National Medical Insurance Plan, extend to cover the treatment of infectious diseases, such as TB making proposal of possible alternatives to these delivery vehicles appropriate. The paper aims to discuss these issues. Design/methodology/approach The analysis includes review of survey results on the extent of medical insurance coverage among migrants as well as legal provisions concerning access to medical care among migrants in Russia and some other migrant-receiving countries. Findings This exercise illuminates the public health risks and economic consequences related to inadequate access to medical help among migrants. Availability of medical insurance even among socially integrated segment of this group is limited. Also of notice is that citizens of Belarus as opposed to others are granted access to the full range of TB services in Russia. Originality/value Using this precedent, the authors propose an alternative mechanism – Inter-State Medical Insurance Fund – to be established by governments of CIS countries, with national allocations covering the provision of medical help to labor migrants from the respective countries in Russia.
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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 | 0.004 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".