Reporting and understanding the safety and adverse effect profile of mobile apps for psychosocial interventions: An update
Bibliographic record
Abstract
Recent years have seen a rapidly increasing trend towards the delivery of health technology through mobile devices. Smartphones and tablet devices are thus becoming increasingly popular for accessing information and a wide range of services, including health care services. Modern mobile apps can be used for a variety of reasons, ranging from education for the patients and assistance to clinicians to delivery of interventions. Mobile phone apps have also been established to benefit patients in a scope of interventions across numerous medical specialties and treatment modalities. Medical apps have their advantages and disadvantages. It is important that clinicians have access to knowledge to make decisions regarding the use of medical apps on the basis of risk-benefit ratio. Mobile apps that deliver psycho social interventions offer unique challenges and opportunities. A number of reviews have highlighted the potential use of such apps. There is a need to describe, report and study their side effects too. The adverse effects associated with these apps can broadly be divided into: (1) those resulting from the security and safety concerns; (2) those arising from the use of a particular psycho social intervention; and (3) those due to the interaction with digital technology. There is a need to refine and reconsider the safety and adverse effects in this area. The safety profile of a mobile PSI app should describe its safety profile in: (1) privacy and security; (2) adverse effects of psychotherapy; and (3) adverse effects unique to the use of apps and the internet. This is, however, a very new area and further research and reporting is required to inform clinical decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".