Design of a Game-Based Training Environment to Enhance Health Care Professionals’ E–Mental Health Skills: Protocol for a User Requirements Analysis
Bibliographic record
Abstract
BACKGROUND: E-mental health (EMH) offers various possibilities for mental health care delivery, with many studies demonstrating its clinical efficacy. However, the uptake of EMH technologies by mental health care professionals remains to be low. One of the reasons for this is the lack of knowledge and skills in using these technologies. Skill enhancement by means of serious gaming has been shown to be effective in other areas but has not yet been applied to the development of EMH skills of mental health care professionals. OBJECTIVE: The aim of this paper is to describe a study protocol for the user requirements analysis for the design of a game-based training environment for mental health care professionals to enhance their skills in EMH. METHODS: The user requirements are formulated using three complementary outputs: personas (lively descriptions of potential users), scenarios (situations that require EMH skills), and prerequisites (required technical and organizational conditions). We collected the data using a questionnaire, co-design sessions, and interviews. The questionnaire was used to determine mental health care professionals' characteristics, attitudes, and skill levels regarding EMH and was distributed among mental health care professionals in the Netherlands. This led to a number of recognizable subuser groups as the basis for personas. Co-design sessions with mental health care professionals resulted in further specification of the personas and an identification of different user scenarios for the game-based training environment. Interviews with mental health care professionals helped to determine the preferences of mental health care professionals regarding training in EMH and the technical and organizational conditions required for the prospective game-based training environment to be used in practice. This combination of requirement elicitation methods allows for a good representation of the target population in terms of both a broad view of user needs (through the large N questionnaire) and an in-depth understanding of specific design requirements (through interviews and co-design). RESULTS: The questionnaire was filled by 432 respondents; three co-design sessions with mental health care professionals and 17 interviews were conducted. The data have been analyzed, and a full paper on the results is expected to be submitted in the first half of 2021. CONCLUSIONS: To develop an environment that can effectively support professionals' EMH skill development, it is important to offer training possibilities that address the specific needs of mental health care professionals. The approach described in this protocol incorporates elements that enable the design of a playful training environment that is user driven and flexible and considers the technical and organizational prerequisites that influence its implementation in practice. It describes a protocol that is replicable and provides a methodology for user requirements analyses in other projects and health care areas. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/18815.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".