Clinical Practice Models for the Use of E-Mental Health Resources in Primary Health Care by Health Professionals and Peer Workers: A Conceptual Framework
Bibliographic record
Abstract
BACKGROUND: Research into e-mental health technologies has developed rapidly in the last 15 years. Applications such as Internet-delivered cognitive behavioral therapy interventions have accumulated considerable evidence of efficacy and some evidence of effectiveness. These programs have achieved similar outcomes to face-to-face therapy, while requiring much less clinician time. There is now burgeoning interest in integrating e-mental health resources with the broader mental health delivery system, particularly in primary care. The Australian government has supported the development and deployment of e-mental health resources, including websites that provide information, peer-to-peer support, automated self-help, and guided interventions. An ambitious national project has been commissioned to promote key resources to clinicians, to provide training in their use, and to evaluate the impact of promotion and training upon clinical practice. Previous initiatives have trained clinicians to use a single e-mental health program or a suite of related programs. In contrast, the current initiative will support community-based service providers to access a diverse array of resources developed and provided by many different groups. OBJECTIVE: The objective of this paper was to develop a conceptual framework to support the use of e-mental health resources in routine primary health care. In particular, models of clinical practice are required to guide the use of the resources by diverse service providers and to inform professional training, promotional, and evaluation activities. METHODS: Information about service providers' use of e-mental health resources was synthesized from a nonsystematic overview of published literature and the authors' experience of training primary care service providers. RESULTS: Five emerging clinical practice models are proposed: (1) promotion; (2) case management; (3) coaching; (4) symptom-focused treatment; and (5) comprehensive therapy. We also consider the service provider skills required for each model and the ways that e-mental health resources might be used by general practice doctors and nurses, pharmacists, psychologists, social workers, occupational therapists, counselors, and peer workers. CONCLUSIONS: The models proposed in the current paper provide a conceptual framework for policy-makers, researchers and clinicians interested in integrating e-mental health resources into primary care. Research is needed to establish the safety and effectiveness of the models in routine care and the best ways to support their implementation.
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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 | 0.048 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| 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".