Locking it down
Bibliographic record
Abstract
OBJECTIVE: To explore the privacy and security of free medication applications (apps) available to Canadian consumers. METHODS: The authors searched the Canadian iTunes store for iOS apps and the Canadian Google Play store for Android apps related to medication use and management. Using an Apple iPad Air 2 and a Google Nexus 7 tablet, 2 reviewers generated a list of apps that met the following inclusion criteria: free, available in English, intended for consumer use and related to medication management. Using a standard data collection form, 2 reviewers independently coded each app for the presence/absence of passwords, the storage of personal health information, a privacy statement, encryption, remote wipe and third-party sharing. A Cohen's Kappa statistic was used to measure interrater agreement. RESULTS: Of the 184 apps evaluated, 70.1% had no password protection or sign-in system. Personal information, including name, date of birth and gender, was requested by 41.8% (77/184) of apps. Contact information, such as address, phone number and email, was requested by 25% (46/184) of apps. Finally, personal health information, other than medication name, was requested by 89.1% (164/184) of apps. Only 34.2% (63/184) of apps had a privacy policy in place. CONCLUSION: Most free medication apps offer very limited authentication and privacy protocols. As a result, the onus currently falls on patients to input information in these apps selectively and to be aware of the potential privacy issues. Until more secure systems are built, health care practitioners cannot fully support patients wanting to use such apps.
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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.161 | 0.066 |
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".