Applications of social marketing for implementation science: a scoping review
Bibliographic record
Abstract
BACKGROUND: Implementation science has a history of drawing from other fields to advance its science, yet understanding how approaches from marketing might enhance the field remains a largely untapped area of theoretical and methodological potential. Social marketing (i.e., applying commercial marketing to solve social or health problems) is a branch of marketing that shares many conceptual features with implementation science (e.g., behaviour change), but remains an unrealized opportunity for synergy. This review aimed to 1) describe studies that have tested social marketing interventions in controlled designs; 2) describe these interventions including their context, mechanism, and outcome; and 3) propose social marketing approaches that might be usefully applied to implementation science. METHODS: This scoping review, with a team consensus discussion, followed JBI (formerly the Joanna Briggs Institute) methodological guidance and included a team of researchers and practitioners in implementation, marketing, and social marketing. Twelve databases were searched. Studies were included that 1) utilized a randomized or non-randomized controlled intervention design; and 2) tested a social marketing intervention as defined by five essential social marketing criteria. Two reviewers independently completed all screening and extraction. Variables extracted included intervention details per social marketing criteria and the intervention's context, mechanism, and outcome. Team consensus discussions of the scoping review results were used to determine approaches that might be usefully applied more broadly across implementation science. RESULTS: Screening of 4,867 citations yielded 28 included studies published from 1999-2023. All topics were from the health field and included nutrition (13, 46%), sexual health/family planning (6, 21%), physical activity (3, 11%), child safety (1, 4%), cancer screening (1, 4%), fall prevention (1, 4%), worksite safety (1, 4%), sanitation (1, 4%), and substance abuse (1, 4%). Novel theories identified included 'Exchange Theory' and 'Consumer Information Processing Model'. Proposed approaches to consider for application included: leverage emotions; design for appeal; consider what your audience values; understand the price; understand the place; emphasize competitive advantage; and use branding. CONCLUSIONS: This review examined the application of social marketing theories and approaches to implementation science. Applying social marketing approaches could invigorate novel and creative thinking in implementation science. REGISTRATION: Open Science Framework Registration link: osf.io/6q834.
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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 | 0.215 | 0.403 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.076 | 0.074 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".