A Game-Based School Program for Mental Health Literacy and Stigma Regarding Depression (Moving Stories): Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The prevalence of elevated depressive symptoms among youth in most western societies is high. Yet, most adolescents who are experiencing depressive symptoms do not seek help. Low mental health literacy, high stigma, and low social support have been shown to hinder help-seeking. A small number of interventions has been developed to target mental health literacy and stigma, but few focus on actual help-seeking and first aid behavior. We have developed a game-based school program called Moving Stories that targets mental health literacy, including knowledge and behavior, and stigma among adolescents, in regard to depression specifically. OBJECTIVE: Our aim is to describe the protocol for a study that will test the effectiveness of the program Moving Stories in a Dutch adolescent sample. We hypothesize that adolescents who participate in the program Moving Stories will have better mental health literacy and less stigma regarding depression compared to adolescents in the nonintervention control group at posttest and at 3- and 6-months follow-up. We also expect a positive change in actual help-seeking and first aid behavior at 3- and 6-months follow-up. METHODS: Moving Stories has been developed by a professional game design company in collaboration with researchers and relevant stakeholders. The effectiveness of Moving Stories will be tested through a randomized controlled trial with two conditions: Moving Stories versus control. Participants will fill in questionnaires at pretest, posttest, and 3- and 6-months follow-up. Our power analysis showed a required sample size of 180 adolescents. RESULTS: Four high schools have agreed to participate with a total of 10 classes. A total of 185 adolescents filled in the pretest questionnaire. The last of the follow-up data was collected in December 2018. CONCLUSIONS: If Moving Stories proves to be effective, it could be implemented as a school-based program to target mental health literacy and stigma regarding depression; this could, in turn, improve early help-seeking in adolescents suffering from depression. TRIAL REGISTRATION: Nederlands Trial Register NTR7033; https://www.trialregister.nl/trial/6855. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11255.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".