Motivational Variables as Moderating Effects of a Web-Based Mental Health Program for University Students: Secondary Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Self-guided web-based interventions have the potential of addressing help-seeking barriers and symptoms common among university students, such as depression and anxiety. Unfortunately, self-guided interventions are also associated with less adherence, implicating motivation as a potential moderator for adherence and improvement for such interventions. Previous studies examining motivation as a moderator or predictor of improvement on web-based interventions have defined and measured motivation variably, producing conflicting results. OBJECTIVE: This secondary analysis of data from a randomized controlled trial aimed to examine constructs of motivation as moderators of improvement for a self-guided 8-week web-based intervention in university students (N=1607). METHODS: Tested moderators included internal motivation, external motivation, and confidence in treatment derived from the Treatment Motivation Questionnaire. The primary outcome was an improvement in depression and anxiety measured by the Depression Anxiety Stress Scale-21. RESULTS: =1.44; P=.15). In this sample, only internal motivation was positively correlated with service initiation, intervention adherence, and intervention satisfaction. CONCLUSIONS: The combination of a web-based intervention and high or moderate internal motivation resulted in greater improvement in the total Depression Anxiety Stress Scale-21 score. These findings highlight the importance of conceptually differentiating motivation-related constructs when examining moderators of improvement. The results suggest that the combination of a web-based intervention and high or moderate internal motivation results in greater improvement. These findings highlight the importance of conceptually differentiating motivation-related constructs when examining moderators of improvement. To better understand the moderating role of internal motivation, future research is encouraged to replicate these findings in diverse samples as well as to examine related constructs such as baseline severity and adherence. Understanding these characteristics informs treatment strategies to maximize adherence and improvement when developing web-based interventions as well as allows services to be targeted to individuals likely to benefit from such interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT04361045; https://clinicaltrials.gov/study/NCT04361045.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".