The Complexity of Mental Health App Privacy Policies: A Potential Barrier to Privacy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: In 2017, the Supreme Court of India ruled that privacy is a fundamental right of every citizen. Although mobile phone apps have the potential to help people with noncommunicable diseases, such as diabetes and mental illness, they often contain complex privacy policies, which consumers may not understand. This complexity may impede the ability of consumers to make decisions regarding privacy, a critical issue due to the stigma of mental illness. OBJECTIVE: Our objective is to determine whether mental health apps have more complex privacy policies than diabetes apps. METHODS: The study used privacy policies extracted from apps. The apps pertained to diabetes or mental health, and were all of Indian origin. Privacy policy reading complexity was compared between the two types of apps using a series of 15 readability measures. The universe of applicable apps on the Google Play store, as viewed between May and June 2017, was considered. The measures of readability were compared using chi-square tests. RESULTS: No significant difference was found between the privacy policy readability of the diabetes apps versus the mental health apps for each of the measures considered. The mean Flesch-Kincaid Grade Level was 13.9 for diabetes apps and 13.6 for mental health apps; therefore, the mean policy grade level for both types of apps was written at a college level. Privacy policies in the 25th percentile of complexity were also written at a college level for both types of apps. CONCLUSIONS: Privacy policy complexity may be a barrier for informed 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it