Barriers and facilitators to dissemination of non-communicable diseases research: a mixed studies systematic review
Bibliographic record
Abstract
Background There is a large number of research studies about the prevention of non-communicable diseases (NCD), with findings taking several years to be translated into practice. One reason for this lack of translation is a limited understanding of how to best disseminate NCD research findings to user-groups in a way that is salient and useful. An understanding of barriers and facilitators to dissemination is key to informing the development of strategies to increase dissemination. Therefore, this review aims to identify and synthesise the barriers and facilitators to dissemination of NCD research findings. Methods A mixed studies systematic review was performed following JBI (formerly known as Joanna Briggs Institute) methodology. The search included articles from January 2000 until May 2021. We conducted a comprehensive search of bibliographic and grey literature of five databases to identify eligible studies. Studies were included if they involved end-users of public health research that were decision-makers in their setting and examined barriers/facilitators to disseminating research findings. Two pairs of reviewers mapped data from included studies against the Framework of Knowledge Translation (FKT) and used a convergent approach to synthesise the data. Results The database search yielded 27,192 reports. Following screening and full text review, 15 studies (ten qualitative, one quantitative and four mixed methods) were included. Studies were conducted in 12 mostly high-income countries, with a total of 871 participants. We identified 12 barriers and 14 facilitators mapped to five elements of the FKT. Barriers related to: (i) the user-group (n = 3) such as not perceiving health as important and (ii) the dissemination strategies (n = 3) such as lack of understanding of content of guidelines. Several facilitators related to dissemination strategies (n = 5) such as using different channels of communication. Facilitators also related to the user-group (n = 4) such as the user-groups’ interest in health and research. Conclusion Researchers and government organisations should consider these factors when identifying ways to disseminate research findings to decision-maker audiences. Future research should aim to build the evidence base on different strategies to overcome these barriers. Systematic review registration The protocol of this review was deposited in Open Science Framework ( https://doi.org/10.17605/OSF.IO/5QSGD ).
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 | 0.128 | 0.328 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.030 | 0.031 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".