An Evaluation of 5-Year Web Analytics for HeadsUpGuys: A Men’s Depression E-Mental Health Resource
Bibliographic record
Abstract
The present study reports an evaluation of web analytics, over a 5-year period, for HeadsUpGuys.org , an eHealth resource for men with depression. Google Analytics, Search Console, and Tag Manager were used to monitor user activity over the course of the website’s first 5 years (June 15, 2015–June 15, 2020). Through this period, HeadsUpGuys had a total of 1,665,356 unique users, amounting to 1,948,481 sessions and 3,328,258 page views. Organic traffic accounted for the highest proportion (53.44%; n = 1,041,277) of website sessions. Four of the top 10 Google search queries that brought users to the website related to suicidality. Three countries (United States, United Kingdom, Canada) accounted for almost three-quarters (71.10%; n = 1,385,485) of the site’s traffic. Nearly three-quarters (73.35%; n = 1,429,285) of sessions occurred on a mobile device. The goal conversion rate for the Self Check was 60.27%. The average time on page was 2 min 53 s, with a bounce rate of 65.92%, and an exit rate of 57.20%. The goal conversion rate for the Stress Test was 52.89%. The average time on page was 4 min 8 s, with a bounce rate of 72.40% and an exit rate of 48.88%. The conversion rate for the final goal was 11.53%, indicating that approximately one in 10 visitors to the site had a session of at least 3 min. The findings illustrate the potential of eHealth resources to support men’s mental health and provide a real-world benchmark to help advance the men’s eHealth field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".