Bibliographic record
Abstract
By using Tajikistan as a case, this study adopted a qualitative approach to understand the different dimensions which make households of migrants with tuberculosis vulnerable to food insecurity. A vulnerability framework was used to identify the risks that tuberculosis poses on households’ availability, accessibility and utilization of food. Then, these risks were analysed in relation to the coping strategies that households employ in order to reduce harm. Data were collected through semi-structured interviews, and observations. Findings highlighted that TB negatively impacts food accessibility, by affecting income-generating activities, labour productivity, and overall expenditure. On the other hand, it affects food utilization, by decreasing patients’ capacity to absorb nourishment and increasing their nutritional requirements. As a result, the gap between nutritional intake required, and household’s ability to access food becomes wider. Households manage the risks posed by tuberculosis by selecting different coping strategies such as borrowing from relatives in migration, taking loans, reducing their expenditures and food consumption, start working, diversify their income, and selling productive assets. As the treatment prolongs, the coping mechanisms employed become more detrimental, compromising their resources. In the long term, the combined effect of being continuously exposed to TB risks, and the negative consequences of the coping mechanisms employed endangers both household’s livelihoods and their food security.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.085 | 0.029 |
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".