Use of Google Analytics to Explore Dissemination Activities for an Online CKD Clinical Pathway: A Retrospective Study
Bibliographic record
Abstract
Background: Data on dissemination strategies that generate awareness of clinical pathways for kidney care are limited. Objective: This study reports the application of Google Analytics to describe the reach and use of the Chronic Kidney Disease Pathway (CKD-P) using a multi-faceted dissemination strategy. Design: The design of this study is a retrospective descriptive study. Setting: This study was conducted in Alberta, Canada. Patients: Individuals who accessed the CKD-P Web site between November 5, 2014, and May 31, 2019. Measurements: Dissemination activities included print, electronic, in-person meetings, and a laboratory prompt. We used Google Analytics over a 5-year period to evaluate the following CKD-P Web site user metrics: number of sessions, pageviews, visit duration, user path, and bounce rate (when an individual visits a single page of the Web site and leaves the Web site without interacting with additional pages). Methods: We plotted dissemination activities alongside Web site metrics using control charts and described the data using means and percentages. We performed chi-square test for trends to evaluate year-over-year usage. Results: There were 83 294 users, 90 805 sessions, and 231 684 pageviews. The overall bounce rate was 45.7%. Each user had an average of 1.5 sessions and a session duration of 2 minutes and 8 seconds. There was a significant positive trend for total annual users ( P = .008), new users ( P = .009), number of sessions ( P = .006), and pageviews per day ( P = .016). Limitations: We were unable to confirm if users were primary care providers and if word-of-mouth dissemination among providers/researchers drove people to use the CKD-P. Conclusions: Google Analytics was a useful and accessible tool for evaluating CKD-P reach and use trends. It was challenging to identify how individual dissemination activities contributed to CKD-P reach; however, repeated dissemination appeared to play a role in increasing CKD-P use. Trial registration: Not applicable—observational study design.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".