Gamified Cognitive Bias Interventions for Psychiatric Disorders: Protocol of a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Cognitive bias modification has been increasingly studied in the past decade with reviews reporting the effectiveness of bias modification. Advances in electronic health and mobile health technologies have transformed how conventional cognitive bias modification is delivered. To date, gamification technologies and serious games have been widely evaluated in health care, and prior studies have reported the use of gamification for cognitive bias modification. However, no prior research, to date, has systematically evaluated the literature for gamified cognitive bias modification interventions. OBJECTIVE: The proposed systematic review aims to review how gamification has been applied to cognitive bias modification interventions. METHODS: A systematic review will be conducted. A search will be conducted on the respective databases till 2018. Selection of the studies will be determined by the Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Quality assessment of the included studies will be assessed using the Cochrane Risk of Bias Tool. In addition, a narrative synthesis will be conducted. RESULTS: We expect that the review will be completed 12 months from the publication of this protocol. CONCLUSIONS: The findings that arise from this review will be crucial as they will inform future research that seeks to integrate gamification technologies into existing conventional bias modification interventions. REGISTERED REPORT IDENTIFIER: RR1-10.2196/10154.
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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 | 0.061 | 0.077 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.009 |
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".