How do we know that they actually use it? Exploring measures of adherence to stress management strategies in university students: A systematic review
Bibliographic record
Abstract
University students are reporting high levels of stress that interfere with their academic performance and daily functioning. In response, higher education institutions have increasingly implemented digital, self-guided stress management resources to provide students with accessible mental health support. While these interventions show promise for improving student wellness, there is a significant gap in our understanding of how students use the strategies taught as part of these resources. This systematic review therefore examined if and how adherence (i.e., strategy use) has been measured in the context of digital self-guided stress management interventions, as well as its associations with stress and other wellness outcomes. Of the 40 studies that met eligibility criteria for the present review, 33 measured adherence (82.5 %). Specifically, nine studies measured frequency (27.2 %), eight measured completion rates (24.2 %), two measured duration (6.1 %), 12 used a combination of these approaches (36.4 %), and two (6.1 %) did not specify which approach was used. Surprisingly, although the majority of studies collected data on adherence, the associations between adherence and stress or other wellness outcomes were scarcely examined. Across studies, adherence was measured using digital analytics and/or self-report; however, barriers were identified in using these methods, including technological issues and challenges in measurement accuracy. Quality assessments revealed a moderate risk of bias. Future research should explore different approaches to enhance adherence measurement accuracy and further examine the link between adherence and wellness outcomes to determine the optimal dose of strategy use for enhancing wellness among university students.
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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 | 0.028 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".