1314-P: Co-creating Gestational Diabetes Education: Using Web Analytics to Assess Learning Interests
Bibliographic record
Abstract
Objectives: Gestational diabetes mellitus (GDM) education is the foundation for self-care management. This study aims to improve the GDM learning experience using the narrative and care experiences of women with GDM and their healthcare providers. Methods: A working group of women with GDM and their health care providers used iterative dialogic priming (key quotes from previous sessions) to frame five sessions. Through a deliberative priority-setting process, participants decided to update www.diabetes-pregnancy.ca, an existing website that addresses GDM education priorities of the group. Participants contributed to the website organization, videos, and text content. Google analytics was used to evaluate site uptake. Results: Five women with GDM and 7 diabetes healthcare providers were involved in updating the content of the website. Following the website re-launch, an analytics assessment of a 50-day average (Nov 14/18 - Jan 3/19) was compared to a previous period with its earlier design (Sept 24 - Nov 13/2018). A total of 490 users visited the site and the majority (n=224) resided in Alberta compared to 546 users (200 from Alberta) in the previous 50-day period. Amongst the recent users with an identified gender (n=166), there were 133 females and 33 males. Bounce rate (visitors immediately leaving website) decreased from 78.4% to 42.4% post-redesign. The pages viewed per user session increased from 1.86 to 3.91, and the average duration of sessions increased by 30.6% (7:23 to 9:39 minutes). Behavior flow of the site showed user navigation to the “GDM” homepage (n=382), followed by the “For Providers” page (n=122), and “Additional Resources” (n=112). Conclusions: Co-creation of online education materials informed uptake of the website. Web analytics suggest that although the audience is more likely female, there are males seeking information. An online GDM resource provides a novel method to support continuous improvement of GDM education. Disclosure J. Boisvenue: None. S.A. Ghnaim: None. P. Kaul: None. E.A. Ryan: None. R.O. Yeung: Advisory Panel; Self; Sanofi. Consultant; Self; Novo Nordisk Inc. Research Support; Self; AstraZeneca. Speaker's Bureau; Self; Novo Nordisk Inc., Sanofi. Funding AstraZeneca Canada
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".