Evaluation of a Canadian Back Pain Mass Media Campaign
Bibliographic record
Abstract
STUDY DESIGN: Quasi-experimental before-and-after design with control group. OBJECTIVE: We evaluated a back pain mass media campaign's impact on population back pain beliefs, work disability, and health utilization outcomes. SUMMARY OF BACKGROUND DATA: Building on previous campaigns in Australia and Scotland, a back pain mass media campaign (Don't Take it Lying Down) was implemented in Alberta, Canada. A variety of media formats were used with radio ads predominating because of budget constraints. METHODS: Changes in back pain beliefs were studied using telephone surveys of random samples from intervention and control provinces before campaign onset and afterward. The Back Beliefs Questionnaire (BBQ) was used along with specific questions about the importance of staying active. For evaluating behaviors, we extracted data from governmental and workers' compensation databases between January 1999 and July 2008. Outcomes included indicators of number of visits to health care providers, use of diagnostic imaging, and compensation claim incidence and duration. Analysis included time series analysis and ANOVA testing of the interaction between province and time. RESULTS: Belief surveys were conducted with a total of 8566 subjects over the 4-year period. Changes on BBQ scores were not statistically significant, however, the proportion of subjects agreeing with the statement, "If you have back pain you should try to stay active" increased in Alberta from 56% to 63% (P = 0.008) with no change in the control group (consistently approximately 60%). No meaningful or statistically significant effects were seen on the behavioral outcomes. CONCLUSION: A Canadian media campaign appears to have had a small impact on public beliefs specifically related to campaign messaging to stay active, but no impact was observed on health utilization or work disability outcomes. Results are likely because of the modest level of awareness achieved by the campaign and future campaigns will likely require more extensive media coverage.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".