Simple Sampling for SARS-CoV-2 Infection in Hidalgo
Bibliographic record
Abstract
Throughout the history of our country, different policies have left an incentive for favorable changes, however, none by itself has managed to combat the problems of chronic malnutrition, to which the current pandemic is added. The state of Hidalgo is in a nutritional transition, with persistent child undernutrition and the predominance of chronic diseases associated with malnutrition (undernutrition, overweight and obesity). Part of this research aims to contribute (in a second phase) to the adequacy of current public policy in the fight against malnutrition and, of course, to the current needs experienced by the SARS-CoV-2 infection contingency. This work develops the application of simple sampling and the stages involved in this statistical tool, whose objective is to establish which part of the reality under study should be studied in order to make inferences about a given population. From the period contemplated between April 28, 2020 and March 8, 2022, the 84 municipalities of the state of Hidalgo reported a total of 86,124 confirmed cases of SARS-CoV-2 infection, from which a sample size of 1,054 subjects has been calculated (representativeness of 91.35% of the target population). The correct application of mathematics in the context of health should allow us to enjoy good health, especially if these results are focused on the promotion and prevention of diseases and their complications; mathematics has surpassed the frontiers of knowledge in various areas and its implementation in this case with respect to public policy and nutrition.
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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 | 0.005 | 0.009 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".