Digitalization of Intervention Delivery and Its Impact on the Effects of Interventions for Mental Well-being in Higher Education Students: Systematic Review and Meta-Analysis Protocol (Preprint)
Bibliographic record
Abstract
BACKGROUND Delivery of interventions within student mental health services has undergone considerable digital transformation in recent years. Traditional face-to-face meetings are being substituted with autonomous digital tools with evident advantages in terms of accessibility and scalability. Despite an increasing array of digital options, there is also a growing recognition that digital tools offer limited effectiveness without some degree of human support. For example, for mental well-being, completely digitally delivered interventions show approximately half the effect sizes of interventions delivered in a traditional format. Blended forms of delivery that use both digital advantages and recognized effects of human contact are therefore promising. Hitherto, the effects of blended delivery have not been evaluated for mental well-being. Hence, investigating how digitalization in intervention delivery impacts intervention effects on mental well-being is important. This is especially relevant among emerging adults enrolled in higher education, going through a critical, transformative life phase. OBJECTIVE This systematic review and meta-analysis will primarily aim to investigate differences in effect due to the degree of digitalization in the mode of delivery of interventions on mental well-being among higher education students. METHODS This work will adhere to the Cochrane Collaboration methodology, and results will be reported according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A systematic literature search will be conducted across 9 databases (Scopus, MEDLINE, PubMed, PsycINFO, ERIC, CINAHL, Web of Science, Cochrane, and International Clinical Trials Registry Platform). The population, intervention, comparator, and outcome framework will inform both the search strategy and eligibility criteria. For inclusion, studies should be randomized controlled trials investigating the effect of individually delivered interventions on positive affect or life satisfaction among mentally healthy higher education students aged 18 to 29 years. Studies will be independently screened, and data will be extracted, including the standardized mean difference as the effect measure. Risk of bias assessment will be conducted using the Cochrane Risk of Bias 2 instrument. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis will be applied, and study biases will be analyzed by funnel-plot assessment and Egger test. Finally, certainty of evidence for positive affect and life satisfaction will be assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. Results will be presented in a summary of findings table. RESULTS The search was finalized in March 2026, generating 6603 records after duplicate removal. Study selection and data extraction were conducted during April 2026, resulting in 41 eligible studies. Risk of bias assessment, data analysis, and manuscript preparation are planned before submission for peer review in August 2026. CONCLUSIONS The principal findings of this study will highlight differences in effect due to the mode of delivery of interventions on mental well-being among higher education students. This will be relevant for the management of student mental health promotion services. CLINICALTRIAL PROSPERO CRD420251131950; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251131950 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/88458
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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: Protocol About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | medium |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.054 | 0.102 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.023 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.004 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".