Communicating Evidence-Based Information on Cancer Prevention to State-Level Policy Makers
Bibliographic record
Abstract
BACKGROUND: Opportunities exist to disseminate evidence-based cancer control strategies to state-level policy makers in both the legislative and executive branches. We explored factors that influence the likelihood that state-level policy makers will find a policy brief understandable, credible, and useful. METHODS: A systematic approach was used to develop four types of policy briefs on the topic of mammography screening to reduce breast cancer mortality: data-focused brief with state-level data, data-focused brief with local-level data, story-focused brief with state-level data, and story-focused brief with local-level data. Participants were recruited from three groups of state-level policy makers-legislative staff, legislators, and executive branch administrators- in six states that were randomly chosen after stratifying all 50 states by population size and dominant political party in state legislature. Participants from each of the three policy groups were randomly assigned to receive one of the four types of policy briefs and completed a questionnaire that included a series of Likert scale items. Primary outcomes-whether the brief was understandable, credible, likely to be used, and likely to be shared-were measured by a 5-point Likert scale according to the degree of agreement (1 = strongly disagree, 5 = strongly agree). Data were analyzed with analysis of variance and with classification trees. All statistical tests were two-sided. RESULTS: Data on response to the policy briefs (n = 291) were collected from February through December 2009 (overall response rate = 35%). All three policy groups found the briefs to be understandable and credible, with mean ratings that ranged from 4.3 to 4.5. The likelihood of using the brief (the dependent variable) differed statistically significantly by study condition for staffers (P = .041) and for legislators (P = .018). Staffers found the story-focused brief containing state-level data most useful, whereas legislators found the data-focused brief containing state-level data most useful. Exploratory classification trees showed distinctive patterns for brief usefulness across the three policy groups. CONCLUSION: Our results suggest that taking a "one-size-fits-all" approach when delivering information to policy makers may be less effective than communicating information based on the type of policy maker.
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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 | 0.099 | 0.267 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".