Perceptions of an AI-Supported Mobile App for Military Health in the Canadian Armed Forces
Bibliographic record
Abstract
Recent developments in technology have expanded its reach and application, particularly when it comes to supporting mental health and well-being. The emergence and popularity of mental health mobile applications have created opportunities to reach those who face challenges with accessing traditional face-to-face supports such as members of the military community. Despite the increasing availability of technology-based mental health supports, perceptions toward this resource is unknown, particularly when it comes to technology that utilizes artificial intelligence (AI). Therefore, the purpose of this study was to evaluate the perceptions of the use of an AI-supported mental health app by the Canadian military community. This qualitative study used a combination of in-depth, semi-structured individual interviews, focus groups, and survey free-text responses. A total of 44 individuals participated in this study including military family members, veterans, health care providers working with veterans, and staff working with military families. The results have been presented in this paper as potential benefits and potential drawbacks of using an AI-supported mental health application. This information may have an important contribution to our growing understanding of how AI-supported technology is perceived through the unique experiences and lens of the military community. Understanding the various perceptions of technology will help inform the direction of expanding mental health services for a population who may be faced with geographic isolation or stigma that affect accessing mental health services. Further research is required to understand how AI-supported mental health applications can be best used with the military community.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".