Health science communication strategies used by researchers with the public in the digital and social media ecosystem: a systematic scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The optimisation of health science communication (HSC) between researchers and the public is crucial. In the last decade, the rise of the digital and social media ecosystem allowed for the disintermediation of HSC. Disintermediation refers to the public's direct access to information from researchers about health science-related topics through the digital and social media ecosystem, a process that would otherwise require a human mediator, such as a journalist. Therefore, the primary aim of this scoping review is to describe the nature and the extent of the literature regarding HSC strategies involving disintermediation used by researchers with the public in the digital and social media ecosystem. The secondary aim is to describe the HSC strategies used by researchers, and the communication channels associated with these strategies. METHODS AND ANALYSIS: We will conduct a scoping review based on the Joanna Briggs Institute's methodology and perform a systematic search of six bibliographical databases (CINAHL, EMBASE, IBSS, PubMed, Sociological Abstracts and Web of Science), four trial registries and relevant sources of grey literature. Relevant journals and reference lists of included records will be hand-searched. Data will be managed using the EndNote software and the Rayyan web application. Two review team members will perform independently the screening process as well as the full-text assessment of included records. Descriptive data will be synthesised in a tabular format. Data regarding the nature and the extent of the literature, the HSC strategies and the associated communication channels will be presented narratively. ETHICS AND DISSEMINATION: This review does not require institutional review board approval as we will use only collected and published data. Results will allow the mapping of the literature about HSC between researchers and the public in the digital and social media ecosystem, and will be published in a peer-reviewed journal.
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 | 0.192 | 0.152 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".