Leveraging technology to improve military mental health: Novel uses of smartphone apps
Bibliographic record
Abstract
Introduction: Smartphones have made promising contributions to the field of military mental health by providing novel app-based approaches to enhance training and deployment, data collection, and creating social domains for participants to share information and perform research. Methods: This article reviews four applications designed specifically for military members and Veterans that increase mental health literacy, overcome barriers to care, and enhance well-being and performance. Results: The Road to Mental Readiness (R2MR) app is an on-the-go training tool based on cognitive behavioural theory (CBT). Unit Victor connects Veterans in a secure chat environment and provides information on available supports. UrMMIND is a pre-deployment tool designed to reinforce healthy behaviours and teach coping techniques. iFeel passively collects and analyses individual smartphone data to detect early signs of depression. Discussion: Mobile apps are playing an ever-increasing role within health care and, when designed and integrated correctly, can yield many benefits. While security and privacy need to be carefully weighed and addressed, they hold the potential to empower the end user in a range of novel ways that were not possible before.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".