Exploring Trends, Pitfalls, and Future Directions in Digital Behaviour Change Interventions for Managing Student Stress
Bibliographic record
Abstract
Student stress poses a widespread challenge, significantly impacting academic performance and mental well-being. Regrettably, stigma and accessibility barriers often discourage students from seeking help. Nonetheless, a ray of hope emerges through persuasive digital interventions (PDIs). These interventions, with their potential to foster positive behaviours in health and wellness, offer a promising avenue to address the complexities of student stress. Understanding how PDIs motivate behavioural change is pivotal for developing effective stress management solutions. This systematic review analyzes papers spanning two decades on PDIs for managing student stress, with the goal of synthesizing methodologies and approaches for designing, developing, and evaluating these interventions. We explore trends, considering factors such as evidence-based therapy, type of stress interventions, digital platforms for delivery, frequently employed persuasive and behaviour change strategies, and evaluation methodology. Additionally, we examine the effectiveness of PDIs in reducing student stress and examine the relationships between effectiveness and design strategies. Finally, our study contributes to the fields of human-computer interaction and mental health by identifying shortcomings and gaps in the existing literature. We propose directions and potential research questions to guide future initiatives in these fields.
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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 | 0.100 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".