Depression Management: A Descriptive Evaluation of Depression Apps in the Google Play Store
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
This research explores how mobile app features and functionality can influence its usage for depression management and overall mental health. It examines the functionalities and features of depression apps associated with the app download count. A search of “Depression” apps carried out in December 2017 using the Google Play Store retrieved 248 apps related to depression. Over 80% of the apps had mainly singular purposes of psychoeducation (36 %), therapeutic treatment (25.2%), medical assessment (18.3%), symptom management (13%), support resources (17%), non-medical functions (14.78%) while forty-six (20%) apps had multiple functions. An app’s number of installs was positively correlated with the rating, number of raters and user interface; but negatively correlated with cost and content rating. Symptom tracking apps were most installed, while medical assessment apps were found not to be the choice apps for Depression management.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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