COVID-19 Response Resource Engagement and User Characteristics of the Wichealth Web-Based Nutrition Education System: Comparative Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: In response to the COVID-19 pandemic, Wichealth launched 4 information resources on the site's user landing dashboard page. These resources were used consistently during the period in which they were available (April 1, 2020, through October 31, 2021); however, only 9% (n=50,888) of Wichealth users eligible for inclusion in the study accessed at least one resource. User engagement with emergency response resources within the context of a web-based health educational tool has not been well investigated due to a paucity of opportunities and a lack of the ability to evaluate relevant users at scale. OBJECTIVE: This investigation was carried out to understand if user characteristics and behaviors measured by the Wichealth web-based education system are associated with a participant's motivation, or lack thereof, to engage with the added COVID-19 resources. METHODS: Sociodemographic characteristics were gathered from Wichealth users with at least one lesson completed and a complete user profile to identify which factors increase the likelihood of user access of any of the Wichealth COVID-19 response resources during the 19-month period between April 1, 2020, and October 31, 2021. A logistic regression analysis was conducted to determine the relative importance of all factors on the likelihood of a user accessing the COVID-19 resources. RESULTS: A total of 50,888 unique Wichealth users included in the study accessed the COVID-19 response resources 66,849 times during the time period. During the same period, 510,939 unique Wichealth users completed at least one lesson about how to engage in healthy behaviors with respect to parent-child feeding but did not access any COVID-19 resources. Therefore, only 9% of Wichealth users who completed a lesson during the time when COVID-19 response resources were available accessed any of the information in those resources. Users of the Spanish language Wichealth version, older users, those less educated, and users with prior Wichealth lesson engagement demonstrated the greatest likelihood of COVID-19 resource use. CONCLUSIONS: This investigation presents findings that demonstrate significant differences between Wichealth users that opted to access COVID-19-specific resources and those who chose not to during their web-based educational session. Reaching users of a web-based educational system with supplemental information may require multiple strategies to increase coverage and ensure the widest possible distribution.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".