An objective approach to evaluating an internet-delivered genetics education resource developed for nurses: using Google Analytics™ to monitor global visitor engagement
Bibliographic record
Abstract
The rapid increase in gene-disease discoveries offers real promise of clinical applications for people and families affected by genetic conditions but for which health professionals are not prepared because of lack of training. The availability of clinically relevant education resources is critical to enabling nurses to develop the appropriate genetics-genomics knowledge and skills to provide optimum care for individuals and families. The Internet is a core resource to support teaching and learning in nurse education. Evaluating such resources is important to maximise the education experience, particularly for subjects traditionally perceived by nurses as being difficult. Telling Stories, Understanding Real Life Genetics is a web-based educational resource. It uses real accounts from individuals and professionals to promote understanding of the impact of genetics-genomics on the lives of people and their families. Google Analytics™ Web analytics service provides time series data for analysing web usage to optimise website effectiveness. We present data of visitor activity and behaviour from 123 countries over three years from 2009–2011 and consider how the application of the web analytics informs approaches to enhancing visibility of the website, provides an indicator of engagement with genetics-genomics both nationally and globally, and informs future expansion of the site as a global resource for health professional education. Telling Stories is an accessible, broad-reaching resource that is of global relevance for health professionals, attracting over 33,500 visitors between 2009–2011, with steadily increasing numbers of returning visitors. The United Kingdom, United States, Canada and the Netherlands are the largest site users. Returning visitors spend significantly more time on site and view more pages than new visitors. Most referring sites are education establishments. More needs to be done now to enhance the site’s accessibility for people of other languages and cultures.
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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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".