Guidance on defining the scope and development of text-based coaching protocols for digital mental health interventions
Bibliographic record
Abstract
A body of literature suggests that the provision of human support improves both adherence to and clinical outcomes for digital mental health interventions. While multiple models of providing human support, or coaching, to support digital mental health interventions have been introduced, specific guidance on how to develop coaching protocols has been lacking. In this Education Piece, we provide guidance on developing coaching protocols for text-based communication in digital mental health interventions. Researchers and practitioners who are tasked with developing coaching protocols are prompted to consider the scope of coaching for the intervention, the selection and training of coaches, specific coaching techniques, how to structure communication with clients and how to monitor adherence to guidelines, and quality of coaching. Our goal is to advance thinking about the provision of human support in digital mental health interventions to inform stronger, more engaging, and effective intervention designs.
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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 | 0.147 | 0.320 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.050 | 0.023 |
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".