A framework to guide storytelling as a knowledge translation intervention for health-promoting behaviour change
Bibliographic record
Abstract
BACKGROUND: Stories can be a powerful tool to increase uptake of health information, a key goal of knowledge translation (KT). Systematic reviews demonstrate that storytelling (i.e. sharing stories) can be effective in changing health-promoting behaviours. Though an attractive KT strategy, storytelling is a complex approach requiring careful planning and consideration of multiple factors. We sought to develop a framework to assist KT researchers and practitioners in health contexts to consider and develop effective KT interventions that include stories or storytelling. METHODS: We conducted a broad search of the literature to identify studies that used storytelling as a KT intervention across different disciplines: health research, education, policy development, anthropology, organizational development, technology research, and media. We extracted purposes, theories, models, mechanisms, and outcomes and then mapped the theoretical and practical considerations from the literature onto the Medical Research Council guidance for complex interventions. The theoretical and practical considerations uncovered comprised the basis of the storytelling framework development. Through discussion and consensus, methodological experts refined and revised the framework for completeness, accuracy, nuance, and usability. RESULTS: We used a complex intervention lens paired with existing behaviour change techniques to guide appropriate theory-based intervention planning and practical choices. An intentional approach to the development of story-based KT interventions should involve three phases. The theory phase specifies the goal of the intervention, mechanisms of action, and behaviour change techniques that will achieve the intended effects. The modelling phase involves development and testing using an iterative approach, multiple methods and engagement of end-users. Finally, formal evaluation using multiple methods helps determine whether the intervention is having its intended effects and value added. CONCLUSIONS: This framework provides practical guidance for designing story-based KT interventions. The framework was designed to make explicit the requisite considerations when determining the appropriateness and/or feasibility of storytelling KT, clarify intervention goals and audience, and subsequently, support the development and testing of storytelling interventions. The framework presents considerations as opposed to being prescriptive. The framework also offers an opportunity to further develop theory and the KT community's understanding of effectiveness and mechanisms of action in storytelling interventions.
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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 | 0.086 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".