Relationship between smoking and health and education spending in Chile
Bibliographic record
Abstract
OBJECTIVE: To estimate the degree to which tobacco consumption is associated with spending on a set of goods and services in Chile, especially health and education, for the total population as well as for specific subgroups. METHODS: A seemingly unrelated regression equation system was used to estimate the statistical relationship between having tobacco expenditures and the budget share allocated to other items for the total population and for specific subgroups in Chile (eg, households within the bottom/top 33% by total expenditures). The use of household-level data allows for the control of a number of sociodemographic characteristics. The nationally representative 2012 Chilean Household Expenditure Survey was used for the analysis. RESULTS: Tobacco consumption is associated with lower budget shares allocated to healthcare, education and housing expenses, especially for poorer households. In the case of health, not consuming tobacco is related to higher health expenditures: up to 32% for the total population. Similarly, in the case of education, not consuming tobacco is statistically related to higher education expenditures: up to 16% for the total population. For all groups, tobacco consumption is also related to a significantly higher budget share allocated to alcoholic beverages. CONCLUSIONS: The strong significant statistical relationship found between tobacco consumption and resources allocated to healthcare and education consumption may be indicative of the existence of a crowding out effect of tobacco. This effect, in turn, may increase the burden that the rest of society must bear for the increased healthcare that they require because of tobacco consumption.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".