Ethical considerations for developing pediatric mhealth interventions for teens with socially complex needs
Bibliographic record
Abstract
TOPIC: Mobile Health (mHealth) stands as a potential means to better reach, assess, and intervene with teens with socially complex needs. These youth often face overlapping adversities including medical illness and a history of experiencing adverse childhood experiences (ACEs). Clinicians are faced with navigating ethical decisions when developing mHealth tools for teens who have socially complex needs. Many tools have been developed for adults from the general population. However, despite the development of thousands of mHealth interventions, developers tend to focus on designing for usability, engagement, and efficacy, with less attention on the ethical considerations of making such tools. PURPOSE: To safely move mHealth interventions from research into clinical practice, ethical standards must be met during the design phase. In this paper we adapt the Four Box Model (i.e., medical indications, preferences of patients, quality of life, and contextual features) to guide mHealth developers through ethical considerations when designing mHealth interventions for teens who present with a medical diagnosis and a history of ACEs. SOURCES: A review of language, inclusive features, data sharing, and usability is presented using both the Four Box Model and potential scenarios to guide each consideration. CONCLUSIONS: To better support designers of mHealth tools we present a framework for evaluating applications to determine overlap with ethical design and are well suited for use in clinical practice with underserved pediatric patients.
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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 | 0.199 | 0.363 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| 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".