Consensus on Use of the Term “App” Versus “Application” for Reporting of mHealth Research
Bibliographic record
Abstract
The number of research studies published which focus on medical “applications” or “apps” continues to grow exponentially. Many academics use these terms interchangeably, however we believe that the discrepancy of terminology used may present a problem for future researchers to systematically identify and conduct appropriate literature searches. We believe it is now time for the mHealth research community to come to a universal consensus and reach a common standard on whether studies should refer to medical “apps” or “applications”. In this article we highlight a number of advantages that standardization of nomenclature will deliver. We also highlight a number of reasons why we believe that mHealth researchers should use the terminology: app [plural-apps]. We conclude by recommending that leading eHealth medical informatics publications such as Journal of Medical Internet Research (JMIR) to implement a policy to utilize common nomenclature moving forward to facilitate improved reporting of studies investigating mobile medical app interventions.
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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 | 0.520 | 0.697 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.015 | 0.024 |
| Research integrity | 0.032 | 0.055 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".