Understanding the Characteristics of Effective Mass Media Campaigns for Back Pain and Methodological Challenges in Evaluating Their Effects
Bibliographic record
Abstract
STUDY DESIGN: Workshop at the Low Back Pain Forum VIII: Primary Care Research on Low Back Pain held in Amsterdam in June 2006. OBJECTIVES: The aim of the workshop was to 1) describe and compare characteristics and outcomes of back pain media campaigns that have taken place internationally; 2) examine general theories of health behavior change from the mass media literature to determine whether it is possible to develop a theoretical framework to explain the observed outcomes; 3) describe the outcome of discussion and expert consensus around lessons learned from these campaigns that may inform the planning and evaluation of future campaigns; and 4) identify priorities for future research. SUMMARY OF BACKGROUND DATA: Mass media campaigns designed to alter societal views about back pain have now been performed in several countries. Although these types of campaigns are an established strategy for delivering preventive health messages, there is limited empirical understanding of the characteristics of effective (or ineffective) health campaigns. METHODS: We reviewed the content and outcome of back pain mass media campaigns conducted in Australia, Norway, and Canada using the Cochrane Effective Practice and Organization of Care Review Group data collection checklist. We also reviewed models of health behavior change that could be used to guide the design, planning, and evaluation of future campaigns. The draft article was reviewed by a group of international back pain experts before forming the basis for discussion at the workshop. Expert comments and those of workshop participants were synthesized and incorporated into the final manuscript. RESULTS: The outcome of discussion and expert consensus around lessons learned from these campaigns are described. CONCLUSION: Our article may help to inform the planning and evaluation of future campaigns and identify priorities for future research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.564 | 0.801 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".