Models, frameworks, and strategies used to implement digital interventions targeted to youth mental health: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The use of digitally enabled technology is considered a promising platform to prevent morbidity and enhance youth mental health as youth are growing up in the digital world and accessing the Internet at increasingly younger age. This scoping review will identify, describe and categorise the models, frameworks and strategies that have been used to study the implementation of digital mental health interventions targeted at youth aged 15-34 years. METHODS AND ANALYSIS: We will conduct a scoping review following the Arksey-O'Malley five-stage scoping review method and the Scoping Review Methods Manual by the Joanna Briggs Institute. Implementation methods will be operationalised according to pre-established aims: (1) process models that describe or guide the implementation process; (2) evaluation frameworks evaluating or measuring the success of implementation; and (3) implementation strategies used in isolation or combination in implementation research and practice. Primary research studies in all languages will be identified in CINAHL, Cochrane Central Register of Controlled Trials, Embase, ERIC, Education Research Complete, MEDLINE and APA PsycINFO on 6 January 2025. Two reviewers will calibrate screening criteria and the data charting form and will independently screen records and abstract data. We will use the Evidence Standards Framework for Digital Health Technologies by the National Institute for Health and Care Excellence to classify digital interventions based on functions, and a pre-established working taxonomy to synthesise conceptually distinct implementation outcomes. Convergent integrated data synthesis will be performed. ETHICS AND DISSEMINATION: Ethical approval is not applicable as this scoping review will be conducted only on data presented in the published literature. Findings will be published and directly infused into our multidisciplinary team of academic researchers, youth partners, health professionals and knowledge users (healthcare and non-governmental organisation decision makers) to co-design and pilot test a digital psychoeducational health intervention to engage, educate and empower youth to be informed stewards of their mental health.
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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 | 0.196 | 0.156 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.065 | 0.018 |
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".