Social Presence and Use of Internet-Delivered Interventions: A Multi-Method Approach
Bibliographic record
Abstract
OBJECTIVE: Internet-delivered interventions can effectively change health risk behaviors and their determinants, but adherence to intervention websites once they are accessed is very low. This study tests whether and how social presence elements can increase website use. METHODS: A website about Hepatitis A, B, and C virus infections was used in a preparatory lab-based eye-tracking study assessing whether social presence elements attract participants' attention, because this is a prerequisite for affecting website use. In the following field study, 482 participants representative of the Dutch population were randomized to either a website with or a website without social presence elements. Participants completed a questionnaire of validated measures regarding user perceptions immediately after exposure to the website. Server registrations were used to assess website use. RESULTS: Participants in the experimental condition focused on the social presence elements, both in terms of frequency (F(1, 98) = 40.34, p<.001) and duration (F(1, 88) = 39.99, p<.001), but did not differ in website use in comparison with the control condition; neither in terms of the number of pages visited (t(456) = 1.44, p = .15), nor in terms of time on the website (t(456) = 0.01, p = .99). CONCLUSIONS: Adding social presence elements did not affect actual use of an intervention website within a public health context. Possible reasons are limited attention for these elements in comparison with the main text and the utilitarian value of intervention websites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".