Gamified Cognitive Control Training for Remitted Depressed Individuals: User Requirements Analysis
Bibliographic record
Abstract
BACKGROUND: The high incidence and relapse rates of major depressive disorder demand novel treatment options. Standard treatments (psychotherapy, medication) usually do not target cognitive control impairments, although these seem to play a crucial role in achieving stable remission. The urgent need for treatment combined with poor availability of adequate psychological interventions has instigated a shift toward internet interventions. Numerous computerized programs have been developed that can be presented online and offline. However, their uptake and adherence are oftentimes low. OBJECTIVE: The aim of this study was to perform a user requirements analysis for an internet-based training targeting cognitive control. This training focuses on ameliorating cognitive control impairments, as these are still present during remission and can be a risk factor for relapse. To facilitate uptake of and adherence to this intervention, a qualitative user requirements analysis was conducted to map mandatory and desirable requirements. METHODS: We conducted a user requirements analysis through a focus group with 5 remitted depressed individuals and individual interviews with 6 mental health care professionals. All qualitative data were transcribed and examined using a thematic analytic approach. RESULTS: Results showed mandatory requirements for the remitted sample in terms of training configuration, technological and personal factors, and desirable requirements regarding knowledge and enjoyment. Furthermore, knowledge and therapeutic benefits were key requirements for therapists. CONCLUSIONS: The identified requirements provide useful information to be integrated in interventions targeting cognitive control in depression.
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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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".