A longitudinal evaluation of web analytics for HeadsUpGuys: A men’s depression e-mental health resource (Preprint)
Bibliographic record
Abstract
BACKGROUND There has been rapid growth in the area of eHealth as a means to delivering tailored health interventions for men. Yet, there have been few attempts at developing eHealth programs specifically oriented to men with depression, and by extension little is known about the uptake and usage patterns of potential end-users. OBJECTIVE The objective of the present study was to conduct a longitudinal evaluation of web analytics for HeadsUpGuys.org, an eHealth resource for men with depression. The study focused on user engagement, traffic sources, and goal conversion (i.e., specific interactivity targets). METHODS Google Analytics, Search Console, and Tag Manager were used to monitor user activity over the course of the website’s first five years (June 15, 2015 – June 15, 2020). These tools were used to harvest data regarding the number of visits, visit duration, bounce rates, most visited pages; traffic filters, country sources, city sources; and goal conversion relating to three specific outcomes [Self Check (depression screen) completion, Stress Test (stress checklist and rating) completion, session of at least 3 minutes]. RESULTS Through its first five years of operation, HeadsUpGuys had a total of 1,665,356 unique users, amounting to 1,948,481 sessions and 3,328,258 pageviews. One in seven visits (14.53%) was from a returning user. Organic traffic accounted for the highest proportion (53.44%) of all the 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%) of the site’s traffic. Nearly three-quarters (73.35%) 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 minutes 53 seconds, 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 minutes 8 seconds, 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 ten visitors to the site had a session of at least 3 minutes. CONCLUSIONS The volume of traffic and the conversion rates affirm the acceptability and usability of HeadsUpGuys. The study illustrates the potential of eHealth resources to support men’s mental health and provides some guidance to advancing the men’s eHealth field.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".