Democratizing access to psychological therapies: Innovations and the role of psychologists.
Bibliographic record
Abstract
Psychological therapies are highly effective interventions for a range of mental health conditions and often preferred by many patients over medication. Unfortunately, most people who could benefit from these therapies do not receive them. This is true even in the United States, which enjoys relatively high numbers of mental health professionals. The lack of access is further compounded by structural inequities, such as income, geography, and race. The low and inequitable access to one of the most effective interventions for mental health conditions is, arguably, one of the most significant barriers to addressing the growing burden of mental health conditions globally. There are several reasons which might contribute to this inequity, notably the historical reliance on complex treatment protocols designed in settings which serve a nonrepresentative group of persons with mental health problems and, consequently, an emphasis on specialist providers and in-person protocols. These factors lead to long and expensive training, variable quality of delivery, and enhanced costs and challenges to patient engagement. In contrast to medication, the lack of a commercial incentive to promote psychological therapies means that there are no market forces which fuel their scaling up. Given there will never be enough psychologists to serve the large unmet and growing mental health needs in the population, we consider stepped and collaborative models that leverage the range of expertise offered by diverse providers, to offer a pathway to scale up a person-centered approach for psychological treatments. In this article, we highlight three innovations that address some barriers and the potential roles of clinical psychologists to broaden the reach of psychological therapies. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".