Promoting Awareness of Key Resources for Evidence-Informed Decision-making in Public Health: An Evaluation of a Webinar Series about Knowledge Translation Methods and Tools
Bibliographic record
Abstract
The National Collaborating Centre for Methods and Tools (NCCMT) has developed several resources to support evidence-informed decision-making - the process of distilling and disseminating best available evidence from research, context, and experience - and knowledge translation, applying best evidence in practice. One such resource, the Registry of Methods and Tools, is a free online database of 195 methods and tools to support knowledge translation. Building on the identification of webinars as a strategy to improve the dissemination of information, NCCMT launched the Spotlight on Knowledge Translation Methods and Tools webinar series in 2012 to promote awareness and use of the Registry. To inform continued implementation of this webinar series, NCCMT conducted an evaluation of the series' potential to improve awareness and use of the methods/tools within the Registry, as well as identify areas for improvement and "what worked." For this evaluation, the following data were analyzed: electronic follow-up surveys administered immediately following each webinar; an additional electronic survey administered 6 months after two webinars; and Google Analytics for each webinar. As of November 2015, there have been 22 webinars conducted, reaching 2048 people in multiple sectors across Canada and around the world. Evaluation results indicate that the webinars increase awareness about the Registry and stimulate use of the methods/tools. Although webinar attendees were significantly less likely to have used the methods/tools 6 months after webinars, this may be attributed to the lack of an identified opportunity in their work to use the method/tool. Despite technological challenges and requests for further examples of how the methods/tools have been used, there is overwhelming positive feedback that the format, presenters, content, and interaction across webinars "worked." This evaluation supports that webinars are a valuable strategy for increasing awareness and stimulating use of resources for evidence-informed decision-making and knowledge translation in public health practice.
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 | 0.353 | 0.456 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".