Stepped Care: A Method to Deliver Increased Access to Psychological Therapies
Bibliographic record
Abstract
OBJECTIVE: To introduce stepped care as a method of organizing the delivery of treatments, and to consider the factors necessary for implementation. METHOD: Stepped care is described within the context of strategies such as collaborative care that aim to increase access to mental health care through the improved coordination of care between primary and specialist mental health services. Results from the implementation of stepped care in the United Kingdom and elsewhere are used to highlight the factors required for introducing stepped care into routine services. Issues to address when implementing high-volume services for common mental health problems are derived from this experience. RESULTS: Stepped care sits within the continuum of organizational systems, from situations where responsibility rests almost entirely with primary care clinicians to systems where all patients are managed by specialists for the entire duration of their treatment. Its core principles of delivering low-burden treatments first, followed by careful patient progress monitoring to step patients up to more intensive treatment, are easy to articulate but lead to considerable implementation diversity when services attempt to work in this manner. Services need to ensure they have specific staff competency training, including skills in delivering evidence-based treatments, access to telephony, and smart patient management informatics systems. CONCLUSIONS: Stepped care can provide the delivery system for supported self-management. To be successful, health systems need high levels of clinical outcome data and appropriately trained workers. Further attention is required to ensure equity of access and to reduce patient attrition in these systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".