Identifying essential factors that influence user engagement with digital mental health tools in clinical care settings: Protocol for a Delphi study
Bibliographic record
Abstract
Introduction: Improving effective user engagement with digital mental health tools has become a priority in enabling the value of digital health. With increased interest from the mental health community in embedding digital health tools as part of care delivery, there is a need to examine and identify the essential factors in influencing user engagement with digital mental health tools in clinical care. The current study will use a Delphi approach to gain consensus from individuals with relevant experience and expertise (e.g. patients, clinicians and healthcare administrators) on factors that influence user engagement (i.e. an essential factor). Methods: Participants will be invited to complete up to four rounds of online surveys. The first round of the Delphi study comprises of reviewing existing factors identified in literature and commenting on whether any factors they believe are important are missing from the list. Subsequent rounds will involve asking participants to rate the perceived impact of each factor in influencing user engagement with digital mental health tools in clinical care contexts. This work is expected to consolidate the perspectives from relevant stakeholders and the academic literature to identify a core set of factors considered essential in influencing user engagement with digital mental health tools in clinical care contexts.
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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 | 0.114 | 0.083 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.046 | 0.011 |
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".