Design of a Game-Based Training Environment to Enhance Mental Health Care Professionals’ Skills in Using e–Mental Health: Multiple Methods User Requirements Analysis
Bibliographic record
Abstract
BACKGROUND: A major factor hampering the adoption of technology in mental health care is a lack of knowledge and skills. Serious gaming offers a potentially effective strategy to enhance the skills needed through experiencing and learning-by-doing in a playful way. However, serious gaming solutions are not widely available for mental health care. Therefore, the development of a game-based training environment in mental health care was pursued in a design project. The first step in such a design project is to identify user requirements that should be met. OBJECTIVE: This study aims to deliver user requirements that inform the design of a game-based training environment for mental health care professionals. This environment aims to support mental health care professionals' knowledge and skill enhancement regarding the use of e-mental health (eMH); for example, video calling, mobile apps, web-based treatment modules, and techniques such as virtual or augmented reality. METHODS: We used an exploratory multiple methods design consisting of a web-based questionnaire, co-design sessions, and interviews. To ensure a good representation of the target user group, professionals from various disciplines within mental health care were included in the research. The multiple methods design facilitates a broad view of user needs and in-depth knowledge of specific design requirements. We describe the protocol for this research project in a protocol paper published in the JMIR Research Protocols in February 2021. RESULTS: The user requirements analysis revealed three types of users for the envisioned game-based training environment: mental health care professionals who want to learn about the basic possibilities of eMH, mental health care professionals who want to develop their eMH skills to the next level, and mental health care professionals who want to experiment with new technologies. This reflects the diversity of needs that were identified, as well as the need to develop a diversity of suitable scenarios in the environment. User requirements analysis shows that the focus of a training environment should be on increasing knowledge about the possibilities of eMH, focusing on experiencing the benefits in particular situations, and building confidence in using eMH in a therapeutic setting. This requires careful consideration of the suitable game characteristics. CONCLUSIONS: Improvement of mental health care professionals' skills in eMH requires an environment that is user driven and flexible, and simultaneously incorporates contextual factors that are relevant for its implementation in practice. This user requirements analysis contributes to the understanding of the issues that should be considered in the development of a game-based training environment. This shows that there are multiple and diverse learning needs among mental health care professionals. Various client populations, services, and situations demand various options for training. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/18815.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".