Supporting Tablet Configuration, Tracking, and Infection Control Practices in Digital Health Interventions: Study Protocol
Bibliographic record
Abstract
BACKGROUND: Tablet-based health care interventions have the potential to encourage patient care in a timelier manner, allow physicians convenient access to patient records, and provide an improved method for patient education. However, along with the continued adoption of tablet technologies, there is a concomitant need to develop protocols focusing on the configuration, management, and maintenance of these devices within the health care setting to support the conduct of clinical research. OBJECTIVE: Develop three protocols to support tablet configuration, tablet management, and tablet maintenance. METHODS: The Configurator software, Tile technology, and current infection control recommendations were employed to develop three distinct protocols for tablet-based digital health interventions. Configurator is a mobile device management software specifically for iPhone operating system (iOS) devices. The capabilities and current applications of Configurator were reviewed and used to develop the protocol to support device configuration. Tile is a tracking tag associated with a free mobile app available for iOS and Android devices. The features associated with Tile were evaluated and used to develop the Tile protocol to support tablet management. Furthermore, current recommendations on preventing health care-related infections were reviewed to develop the infection control protocol to support tablet maintenance. RESULTS: This article provides three protocols: the Configurator protocol, the Tile protocol, and the infection control protocol. CONCLUSIONS: These protocols can help to ensure consistent implementation of tablet-based interventions, enhance fidelity when employing tablets for research purposes, and serve as a guide for tablet deployments within clinical settings.
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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 | 0.055 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".