Systematic Review on Information Technology Approaches to Evaluate the Impact of Public Health Campaigns: Real Cases and Possible Directions
Bibliographic record
Abstract
Evaluating the success of a public health campaign is critical. It helps policy makers to improve prevention strategies and close existing gaps. For instance, Brazil's "Syphilis No!" campaign reached many people, but how do we analyze its real impact on population awareness? Are epidemiologic variables sufficient? This study examined literature on using of information technology approaches to analyze the impact of public health campaigns. We began the systematic review with 276 papers and narrowed it down to 17, which analyzed campaigns. In addition to epidemiological variables, other types of variables of interest included: level of (i) access to the campaign website, (ii) subject knowledge and awareness, based on questionnaires, (iii) target population's interest, measured from both online search engine and engagement with Social Network Service, and (iv) campaign exposure through advertising, using data from television commercials. Furthermore, we evaluated the impact by considering several dimensions such as: communication, epidemiology, and policy enforcement. Our findings provide researchers with an overview of various dimensions, and variables-of-interest, for measuring public campaign impact, and examples of how and which campaigns have used them.
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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 | 0.045 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".