Developing an explanatory theoretical model for engagement with a web-based mental health platform: results of a mixed methods study
Bibliographic record
Abstract
BACKGROUND: With the growing need for accessible, high-quality mental health services, especially during the COVID-19 pandemic, there has been increasing development and uptake of web-based interventions in the form of self-directed mental health platforms. The Big White Wall (BWW) is a web-based platform for people experiencing mental illness and addiction that offers a range of evidence-based self-directed treatment strategies. Drawing on existing data from a large-scale evaluation of the implementation of BWW in Ontario, Canada (which involved a pragmatic randomized controlled trail with an embedded qualitative process evaluation), we sought to investigate the influences on the extent to which people engage with BWW. METHODS: In this paper we drew on BWW trial participants' usage data (number of logins) and the qualitative data from the process evaluation that explored participants' experiences, engagement with and reactions to BWW. RESULTS: Our results showed that there were highly complex relationships between the influences that contributed to the level of engagement with BWW intervention. We found that a) how people expected to benefit from using a platform like BWW was an important indicator of their future usage, b) moderate perceived symptoms were linked with higher engagement; whereas fewer actual depressive symptoms predicted use and anxiety had a positive linear relationship with usage, and that c) usage depended on positive early experiences with the platform. CONCLUSIONS: Our findings suggest that the nature of engagement with platforms such as BWW is not easily predicted. We propose a theoretical framework for explaining the level of user engagement with BWW that might also be generalizable to other similar platforms.
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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 | 0.066 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".