An Emotional Bias Modification for Children With Attention-Deficit/Hyperactivity Disorder: Co-design Study
Bibliographic record
Abstract
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is one of the common neurodevelopment disorders. Children with ADHD typically have difficulties with emotional regulation. Previous studies have investigated the assessment for underlying emotional biases using the visual probe task. However, one of the significant limitations of the visual probe task is that it is demanding and repetitive over time. Previous studies have examined the use of gamification methods in addressing the limitations of the emotional bias visual probe task. There has also been increased recognition of the potential of participatory action research methods and how it could help to make the conceptualized interventions more relevant. OBJECTIVE: The primary aim of this study was to collate health care professionals' perspectives on the limitations of the existing visual probe task and to determine if gamification elements were viable to be incorporated into an emotional bias modification task. METHODS: A co-design workshop was conducted. Health care professionals from the Department of Development Psychiatry, Institute of Mental Health, Singapore, were invited to participate. Considering the COVID-19 pandemic and the restrictions, a web-based workshop was conducted. There were 3 main phases in the workshops. First, participants were asked to identify limitations and suggest potential methods to overcome some of the identified limitations. Second, participants were shown examples of existing gaming interventions in published literature and commercial stores. They were also asked to comment on the advantages and limitations of these interventions. Finally, participants were asked if gamification techniques would be appropriate. RESULTS: Overall, 4 health care professionals consented and participated. Several limitations were identified regarding the conventional emotional bias intervention. These included the nature of the task parameters, included stimulus set, and factors that could have an impact on the accuracy of responding to the task. After examining the existing ADHD games, participants raised concerns about the evidence base of some of the apps. They articulated that any developed ADHD game ought to identify the specific skill set that was targeted clearly. Regarding gamification strategies, participants preferred economic and performance-based gamification approaches. CONCLUSIONS: This study has managed to elucidate health care professionals' perspectives toward refining a conventional emotional bias intervention for children with ADHD. In view of the repetitiveness of the conventional task, the suggested gamification techniques might help in influencing task adherence and reduce the attrition rates.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".