Twitter as a Knowledge Translation Tool to Increase Awareness of the OpenHEARTSMAP Psychosocial Assessment and Management Tool in the Field of Pediatric Emergency Mental Health
Bibliographic record
Abstract
Rationale The increasing prevalence of pediatric mental health presentations in pediatric emergency departments (PED) requires improved integration of evidence-based management strategies. Social media, specifically Twitter, has shown potential to aid in closing the knowledge translation (KT) gap between these evidence-based management strategies and pediatric emergency medicine (PEM) providers. Aims and objectives The primary outcome of this study is to evaluate the effectiveness of Twitter as a KT dissemination tool in PEM. The exploratory outcomes were to assess how to effectively implement Twitter in KT, explore ways in which Twitter can maximize the global reach of OpenHEARTSMAP and whether Twitter can lead to increased adoption of OpenHEARTSMAP. Methods A one-week prospective promotion on Twitter was conducted to disseminate the OpenHEARTSMAP tool using 15 topic-related hashtags (arm 1, 15 Tweets) versus one post wherein 15 different Twitter users were mentioned in 15 different comments (arm 2, 1 Tweet). A one-week control period immediately prior to posting was employed for comparisons. Results During the Twitter week, visits per day to OpenHEARTSMAP increased by 175%; mean time spent on the website increased by 212%; and mean page actions per visit increased by 130%. The greatest increase in visits occurred on the first day of Tweeting. Arm 2 received the greatest engagements. Within arm 1, the category of pediatrics received the most engagements (hashtag #Peds was most popular). Arm 1 received 455 impressions compared to 2071 in arm 2. No new users registered an account on the OpenHEARTSMAP website, which is required to physically use the tool. Conclusion Twitter can be an effective KT tool to increase awareness of research, the first step of KT, in the domain of PEM mental health care. Strategies for success include building a robust Twitter following; posting during peak healthcare-related Twitter traffic times; employing hashtags coinciding with current events; and targeting posts by tagging users who need not necessarily be generally well-known opinion leaders.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".