What public health interventions do people in Canada prefer to fund? A discrete choice experiment
Bibliographic record
Abstract
OBJECTIVE: To assess public support of tailored and targeted public health interventions for marginalized communities. METHODS: We conducted a discrete choice experiment using a web-based survey advertised to Facebook and Instagram users living in Canada, aged > 16. Participants were asked to choose between funding two hypothetical public health programs. Each program was described by its purpose; expected increase in life expectancy; and target audience. Demographically weighted generalized linear mixed-effects models were constructed to identify program factors associated with program selection. RESULTS: Participants completed up to 8 discrete choice comparison exercises each resulting in 23,889 exercises were completed by 3054 participants. Selected programs were less likely to focus on prevention (vs. treatment). For each 1-year increase in the marginal years of life gained, there was a 15% increase in the odds of a program being selected. Interventions tailored to marginalized communities or targeting stigmatized health conditions were less likely to be selected compared to interventions targeted to the general population or targeting chronic health conditions. Noteworthy exceptions included an increased preference for interventions aligning with the perceived needs or cultural expectations for marginalized communities. CONCLUSIONS: Stigmatizing perceptions of health conditions and key populations likely influence public health programming preferences of Canadians. PUBLIC HEALTH IMPLICATIONS: Informational campaigns highlighting disparities experienced by marginalized populations may improve support for targeted and tailored interventions.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".