Addressing vaccine hesitancy: The potential value of commercial and social marketing principles and practices
Bibliographic record
Abstract
Many countries and communities are dealing with groups and growing numbers of individuals who are delaying or refusing recommended vaccinations for themselves or their children. This has created a need for immunization programs to find approaches and strategies to address vaccine hesitancy. An important source of useful approaches and strategies is found in the frameworks, practices, and principles used by commercial and social marketers, many of which have been used by immunization programs. This review examines how social and commercial marketing principles and practices can be used to help address vaccine hesitancy. It provides an introduction to key marketing and social marketing concepts, identifies some of the major challenges to applying commercial and social marketing approaches to immunization programs, illustrates how immunization advocates and programs can use marketing and social marketing approaches to address vaccine hesitancy, and identifies some of the lessons that commercial and non-immunization sectors have learned that may have relevance for immunization. While the use of commercial and social marketing practices and principles does not guarantee success, the evidence, lessons learned, and applications to date indicate that they have considerable value in fostering vaccine acceptance.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".