Mobile Health for All: Public-Private Partnerships Can Create a New Mental Health Landscape
Bibliographic record
Abstract
Research has already demonstrated that different mHealth approaches are feasible, acceptable, and clinically promising for people with mental health problems. With a robust evidence base just over the horizon, now is the time for policy makers, researchers, and the private sector to partner in preparation for the near future. The Lifeline Assistance Program is a useful model to draw from. Created in 1985 by the U.S. Federal Communications Commission (FCC), Lifeline is a nationwide program designed to help eligible low-income individuals obtain home phone and landline services so they can pursue employment, reach help in case of emergency, and access social services and healthcare. In 2005, recognizing the broad shift towards mobile technology and mobile-cellular infrastructure, the FCC expanded the program to include mobile phones and data plans. The FCC provides a base level of federal support, but individual states are responsible for regional implementation, including engagement of commercial mobile phone carriers. Given the high rates of disability and poverty among people with severe mental illness, many are eligible to benefit from Lifeline and research has shown that a large proportion does in fact use this program to obtain a mobile phone and data plan. In the singular area of mobile phone use, the gap between people with severe mental illness and the general population in the U.S. is vanishing. Strategic multi-partner programs will be able to grant access to mHealth for mental health programs to those who will not be able to afford them-arguably, the people who need them the most. Mobile technology manufacturing costs are dropping. Soon all mobile phones in the marketplace, including the more inexpensive devices that are made available through subsidy programs, will have "smart" capabilities (ie, internet connectivity and the capacity to host apps). Programs like Lifeline could be expanded to include mHealth resources that capitalize on "smart" functions, such as secure/encrypted clinical texting programs and mental health monitoring and illness-management apps. Mobile phone hardware and software development companies could be engaged to add mHealth programs as a standard component in the suite of tools that come installed on their mobile phones; thus, in addition to navigation apps, media players, and games, the new Android or iPhone could come with guided relaxation videos, medication reminder systems, and evidence-based self-monitoring and self-management tools. Telecommunication companies could be encouraged to offer mHealth options with their data plans. Operating system updates pushed out by the mobile carrier companies could come with optional mHealth applications for those who elect to download them. In the same manner in which the Lifeline Assistance Program has helped increase access to fundamental opportunities to so many low-income individuals, innovative multi-partner programs have the potential to put mHealth for mental health resources in the hands of millions in the years ahead.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.003 | 0.039 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.059 | 0.009 |
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".