Building the Infrastructure for Sustainable Digital Mental Health: It’s ‘Prime Time’ for Implementation Science (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Despite growing enthusiasm and a proliferation of digital mental health innovations, their integration into routine clinical care remains limited, often stalled at the pilot, research, or demonstration stage. This editorial argues that successful adoption, scale-up, and sustainability of digital mental health innovations require intentional infrastructure, not just technology. Using the NASSS (Nonadoption, Abandonment, Scale-up, Spread, and Sustainability) implementation science framework, we examine how challenges across the seven framework domains (condition, technology, value proposition, adopters, organization, wider context, and their interactions over time) continue to hinder meaningful progress. We describe a focused digital mental innovation infrastructure as a model for overcoming these barriers. Drawing on experiences from the Digital Innovation Hub at Canada’s largest mental health and addictions teaching hospital, we illustrate how investing in the right infrastructure may move digital mental health innovations from ‘promising’ to ‘impactful’. We call for global collaboration to share knowledge and accelerate real-world integration of digital innovations in routine mental health clinical care.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".