Research Dissemination Strategies in Pediatric Emergency Care Using a Professional Twitter (X) Account: A Mixed Methods Developmental Study of a Logic Model Framework
Bibliographic record
Abstract
BACKGROUND: Research dissemination is a vital step in bridging the gap between the publication of cutting-edge research and its adoption into clinical practice. Social media platforms like Twitter (rebranded as X) offer promising channels for dissemination, yet research organizations lack clear guidance on establishing a professional social media presence. We present a structured framework based on our research network's multiyear experience developing a Twitter account for research dissemination. OBJECTIVE: This study aimed to provide a roadmap for organizations aiming to create a professional Twitter account for research dissemination. METHODS: This was a mixed methods study analyzing the Pediatric Emergency Care Applied Research Network (PECARN) Twitter team's 4-year experience (2020-2023) with building a social media account. Using the nominal group technique qualitative approach, we recorded insights from the 6 team members' experiences in a round-robin fashion until response saturation. In addition, we analyzed internal Slack (Slack Technologies) communications to identify key developmental events. Together, these were then prioritized by consensus to elucidate key developmental events that enhanced both social media and scientific engagement. This process was informed by quantitative data from Twitter performance metrics and Altmetric Attention Scores for journal publications collected over a 39-month period. Together, these elements informed the design of a logic model framework. RESULTS: The nominal group technique generated 63 thematic statements which included issues such as organizational structure, content strategy, technologies, analytics, organizational priorities, and challenges. These statements coalesced into the 7 domains (priorities, assumptions, inputs, outputs, outcomes, and external factors) that comprise the logic model. Inputs included organizational support (eg, executive-level champion and funding), specialized personnel (eg, content writer and analytics manager), and operational technologies (eg, communications and data analytics tools). Outputs encompassed targeted activities, such as engaging with other Twitter accounts, publishing high-quality tweets highlighting scholarly work, and developing a dynamic operations manual for the Twitter team. Outcomes were measured through tweet metrics, account analytics, and article-level impact scores. CONCLUSIONS: Our logic model roadmap, based on our practical multiyear experience and data-driven strategies, can serve as a guide for research organizations or medical institutions aiming to incorporate Twitter or other social media platforms for research dissemination.
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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 | 0.171 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".