Examining the Use of Mobile Technology to Deliver Tailored Sexual Assault Prevention in a Classroom Environment in the Military: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Advances in mobile technology over the last 10 years have expanded its use in scientific research. However, there are challenges in creating a reliable system for intervention content delivery and data collection in an environment with limited internet connectivity and limited staffing capacity. The Sexual Communication and Consent (SCC) study used tablets to provide individualized Sexual Assault Prevention and Response training in a classroom environment that was both technologically and support staff limited. OBJECTIVE: We developed the SCC Basic Military Training app and a separate Sexual Assault Response Coordinator app to support individualized training within the new SCC program. This paper presents the functionality, protocols, challenges, and feasibility of deploying mobile technology in an educational environment in the military with limited resources. METHODS: We developed both mobile apps targeting the Apple iOS ecosystem. The Basic Military Training app provided a screening instrument that routed the trainee into 1 of 5 specific intervention programs. Over 2 days of basic military training set 2 weeks apart, trainees received a combined 6 hours of program-specific tablet training, combined with universal, interactive classroom training, led by qualified instructors. The Sexual Assault Response Coordinator app, used to deliver supplemental content to a subgroup of trainees, was made available for voluntary and private use at the Sexual Assault Response Coordinator's office on base. All anonymous data were manually transferred onto laptops, where the data were aggregated into files and securely transferred to the project staff for analysis. The study was conducted at the Lackland Air Force Base, Joint Base San Antonio, with 9196 trainees providing the data. RESULTS: A total of 7742 trainees completed both the sessions of the SCC program and a series of evaluative assessments. Some trainees did not receive day 2 training, and only received day 1 training because the COVID-19 pandemic shortened the study period. Of the 190 SCC classes taught, only one class was unable to complete tablet training because of Apple licensing-related technology failure. The 360 study tablets were distributed across 3 classrooms (120 per classroom) and were handled at least 16,938 times with no reports of breakage or requiring replacement. Wi-Fi access limitations exacerbated the complexity of Apple licensing revalidation and the secure transfer of data from the classroom to project personnel. The instructor staff's limited technical knowledge to perform certain technical tasks was challenging. CONCLUSIONS: The results demonstrated the feasibility of deploying a mobile app for tablet-based training in a military educational environment. Although successful, the study was not without technical challenges. This paper gives examples of technical lessons learned and recommendations for conducting the study differently, with the aim that the knowledge gained may be helpful to other researchers encountering similar requirements.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".