Disseminating the Foundations of Knowledge Translation and Patient Engagement Science Through the KnowledgeNudge Blog and Twitter Profile: Quantitative Descriptive Evaluation
Bibliographic record
Abstract
BACKGROUND: There is a documented need to build capacity for theory- and evidence-informed knowledge translation (KT) and patient engagement (PE) practice in health research. Dissemination of foundational content online coupled with social media promotion may build capacity by increasing awareness, knowledge, and positive attitudes. OBJECTIVE: This retrospective study sought to (1) describe exposure and engagement of the KnowledgeNudge KT and PE dissemination strategy (online blog and Twitter profile) over 2 years and (2) identify and compare characteristics of individual posts with the most and least exposure and reach. METHODS: Exposure was assessed by blog site views per month and Twitter profile impressions per month. Engagement was assessed by Twitter profile interactions per month. Descriptive statistics were calculated for 6-month blocks and compared using one-way analysis of variance or Student t test. Individual post exposure was assessed by average post views per week. Individual post reach was assessed by average post reads per week. High- and low-profile blog posts with the highest and lowest 10th percentile for exposure and reach were identified. RESULTS: A total of 99 posts and 755 tweets were published during the study period. There was a significant increase in exposure (P=.004) and reach (P<.001) during the final 6 months. Seven high-profile and 6 low-profile posts were identified. High-profile posts had a significantly greater average word count than low-profile posts (P=.003). There were no other significant differences between posts. CONCLUSIONS: The increases in KnowledgeNudge exposure and engagement offer preliminary evidence in support of this dissemination strategy for the practice of KT and PE. Variation in individual post exposure and reach warrants further exploration to tailor content to user needs. Future work will include a prospective evaluation strategy to explore the effect of KnowledgeNudge on awareness, knowledge, attitudes, and behavior.
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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 | 0.050 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".