Bibliographic record
Abstract
Social marketing is acquiring a familiar ring to people in the health sector. The UK government's recent public health white paper talks of the "power of social marketing" and "marketing tools applied to social good [being] used to build public awareness and change behaviour." This has led to the formation of the National Social Marketing Centre for Excellence, a collaboration between the Department of Health and the National Consumer Council. The centre will develop the first social marketing strategy for health in England. Similarly, the Scottish Executive recently commissioned an investigation into how social marketing can be used to guide health improvement. Australia, New Zealand, Canada, and the United States all have social marketing facilities embedded high within their health services. Evans has outlined social marketing's basic precepts. We develop some of these ideas and suggest how social marketing can help doctors and other health professionals to do their jobs more effectively.
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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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".