Potential social marketing applications for knowledge translation in healthcare: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Knowledge translation has emerged as a practice and a science to bridge the gap between evidence and practice in healthcare. While the field has appropriately borrowed from other related fields to advance its science, there remain fields less mined. One such field with potential relevance to knowledge translation, but limited application to date, is social marketing. This review aims to determine elements of social marketing interventions that could be applied to knowledge translation science. Our objectives are to: (1) summarise the types of studies that have tested social marketing interventions in controlled intervention study designs; (2) describe the social marketing interventions and their effects; and (3) propose strategies for the integration of social marketing interventions into knowledge translation science. METHODS AND ANALYSIS: This scoping review will be conducted using the Joanna Briggs Institute Methodological Guidance. For the first and second objectives, all English-language studies published from 1971 onwards will be included if they (1) used a randomised or non-randomised controlled intervention design, and (2) tested a social marketing intervention as defined by five essential social marketing criteria. The research team will address the third objective through discussion and consensus. All screening and extraction will be performed independently by two reviewers. Variables extracted will include intervention details using essential and desirable social marketing criteria and the context, mechanism and outcomes of the interventions. ETHICS AND DISSEMINATION: This project is a secondary analysis of published papers and does not require ethics approval. We will disseminate our review outputs in knowledge translation journals and present at relevant conferences across the spectrum of the field. We will produce a short and long version of a plain language summary that will be tailored to various groups including implementation scientists and quality improvement researchers. REGISTRATION DETAILS: Open Science Framework Registration link: osf.io/6q834.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.224 | 0.204 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.075 | 0.026 |
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".