Stepped care, stepped care “lite” & matching intervention components to individual mental health needs: A rapid scoping review of mental health and substance use interventions for post-secondary students
Bibliographic record
Abstract
PURPOSE: Stepped Care Models (SCM) and other approaches for organizing the delivery of services and resources by individual mental health (MH) needs are being increasingly implemented in post-secondary institutions. However, no consensus definitions exist of what constitutes a SCM for post-secondary students (PSS), and there is little guidance for evaluation of these complex, multicomponent interventions. The purpose of this research is to identify and characterize MH and substance use interventions for PSS that apply a SCM, stepped approach (i.e., stepped care "lite"), and/or organize delivery of resources/services based on individual MH needs. METHODS: A rapid scoping review of peer-reviewed research articles was conducted using OVID MEDLINE®, OVID Embase, EBSCO CINAHL, OVID PsycINFO®, and ERIC. Eligible studies included multicomponent interventions for improving MH or substance use among PSS applying a SCM, stepped approach or another way of organizing resources/services offered according to individual MH needs. Results: 5757 abstracts were reviewed, resulting in full text examination of 172 studies. Data were extracted from 68 eligible studies comprising 50 interventions (SCMs: n = 7, stepped care "lite": n = 13; organized delivery matched to MH needs: n = 30). Almost all involved a website/app and symptom tracking was often included within the intervention. Most addressed either alcohol use, depression, anxiety and eating disorders. A variety of evaluation models were applied, but approaches were not generally geared to look at individual-level outcomes in a manner that captured the overall effect of the SCM or outcomes related to the specific "dose" of the intervention received. Most outcomes focused on MH symptoms, satisfaction, and utilization; student-related outcomes such as academic success were rarely used. Student co-design was not often described. CONCLUSIONS/IMPLICATIONS: Despite increasing implementation of SCMs in post-secondary settings, few studies on the model have been published. Drawing on strengths and shortcomings of studies identified, recommendations for future work in this area are presented.
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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 | 0.035 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".