Creating a social media strategy for an international cardiothoracic research network: a scoping review
Bibliographic record
Abstract
AIMS: A cardiac surgery international nursing and allied professional research network titled CONNECT was created to strengthen collaborative cardiac surgery research through shared initiatives including supervision, mentorship, workplace exchange programs, and multi-site clinical research. As with any new initiative, there is a need to build brand awareness to enhance user familiarity, grow membership, and promote various opportunities offered. Social media has been used across various surgical disciplines; however, their effectiveness in promoting scholarly and academic-based initiatives has not been examined. The aim of this scoping review was to examine the different types of social media platforms and strategies used to promote cardiac research initiatives for CONNECT. METHODS AND RESULTS: A scoping review was undertaken in which a comprehensive and thorough review of the literature was performed. Fifteen articles were included in the review. Twitter appeared to be the most common form of social media used to promote cardiac initiatives, with daily posts being the most frequent type of engagement. Frequency of views, number of impressions and engagement, link clicks, and content analysis were the most common types of evaluation metrics that were identified. CONCLUSION: Findings from this review will inform the design and evaluation of a targeted Twitter campaign aimed at increasing brand awareness of CONNECT, which will include the use of @CONNECTcardiac Twitter handle, hashtags, and CONNECT-driven journal clubs. In addition, the use of Twitter to disseminate information and brand initiatives related to CONNECT will be evaluated using the Twitter Analytics function. REGISTRATION: Open Science Framework: osf.io/q54es.
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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 | 0.043 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.032 | 0.024 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".