Mental Health Messages in Prominent Mental Health Apps
Bibliographic record
Abstract
PURPOSE: Many who seek primary health care advice about mental health may be using mobile applications (apps) claiming to improve well-being or relieve symptoms. We aimed to identify how prominent mental health apps frame mental health, including who has problems and how they should be managed. METHODS: We conducted a qualitative content analysis of advertising material for mental health apps found online in the United States, the United Kingdom, Canada, and Australia during late 2016. Apps were included if they explicitly referenced mental health diagnoses or symptoms and offered diagnosis and guidance, or made health claims. Two independent coders analyzed app store descriptions and linked websites using a structured, open-ended instrument. We conducted interpretive analysis to identify key themes and the range of messages. RESULTS: We identified 61 mental health apps: 34 addressed predominantly anxiety, panic, and stress (56%), 16 addressed mood disorders (26%), and 11 addressed well-being or other mental health issues (18%). Apps described mental health problems as being psychological symptoms, a risk state, or lack of life achievements. Mental health problems were framed as present in everyone, but everyone was represented as employed, white, and in a family. Explanations about mental health focused on abnormal responses to mild triggers, with minimal acknowledgment of external stressors. Therapeutic strategies included relaxation, cognitive guidance, and self-monitoring. Apps encouraged frequent use and promoted personal responsibility for improvement. CONCLUSIONS: Mental health apps may promote medicalization of normal mental states and imply individual responsibility for mental well-being. Within the health care clinician-patient relationship, such messages should be challenged, where appropriate, to prevent overdiagnosis and ensure supportive health care where needed.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".