Evaluating the Safety and Efficacy of Mental Health Apps for Patients on Waiting Lists
Bibliographic record
Abstract
This report has been prepared by Research Associates from the McMaster Research Shop at the request of the Suicide Prevention Community Council of Hamilton (SPCCH). The SPCCH is exploring interventions to support those in acute mental health crises often on long waiting lists for mental health treatment. They see the potential for mental health apps on smartphones to provide accessible and effective support, but it's unclear which smartphone apps (if any) are considered safe and effective, as judged by mental health professionals. As such, this research intended to evaluate existing (and prominent) mental health apps to propose a shortlist of apps to patients at risk of suicide waiting to be seen clinically. This report draws on academic and grey literature about existing mental health apps and evaluative frameworks from largely Canadian and American contexts to offer an evidence-based starting point for app evaluation. It also draws on the clinical expertise of two key informants to support the development of evaluation criteria and an overall understanding of both the opportunities and challenges for mental health apps in the treatment and management of diverse mental health concerns.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".