Mapping Colombians’ positions on national policies to control tobacco and marijuana consumption: a pilot study
Bibliographic record
Abstract
BACKGROUND: Public authorities use a variety of control policies, with varying degrees of severity, to reduce the prevalence of health-damaging habits. Since these policies can only succeed if people understand and approve of them, this study mapped Colombians' positions on policies to control tobacco and marijuana consumption. METHOD: A sample of 147 adults was presented with 32 vignettes illustrating a control policy. Each vignette contained four items of information: the behavior targeted (smoking tobacco or using marijuana), the nature of preventive measures (e.g., information campaigns), the degree of regulatory measures (e.g., prohibition of use by minors) and the severity of penalties (e.g., imprisonment). RESULTS: Through cluster analysis, three qualitatively different positions were found in relation to control policies for each substance: Generally unfavorable, irrespective of policy (22% and 17%), Depends on regulation (18% and 22%), and Always favorable, irrespective of policy (23% and 25%). A substantial minority of participants (37% and 36%) expressed no opinion at all. CONCLUSION: While qualitatively different positions on the acceptability of national policies to control tobacco and marijuana consumption were indeed observed among Colombian participants, the most frequent response seemed to be indifference (or indeterminacy), with other positions reflecting little more than systematic opposition or blind acquiescence. It would therefore be useful to make citizens aware that their opinions count, that their relative indifference to these issues is in itself a problem, and that it is by taking their perspectives into account that one can truly define and make effective public health policies that are understood and accepted by as many people as possible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".