ATA Practice Guidelines for Video-Based Online Mental Health Services
Bibliographic record
Abstract
Table of Contents PREAMBLE SCOPE INTRODUCTION Internet-Based Telemental Health Models of Care Today CLINICAL GUIDELINES A. Professional and Patient Identity and Location 1. Provider and Patient Identity Verification 2. Provider and Patient Location Documentation 3. Contact Information Verification for Professional and Patient 4. Verification of Expectations Regarding Contact Between Sessions B. Patient Appropriateness for Videoconferencing-Based Telemental Health 1. Appropriateness of Videoconferencing in Settings Where Professional Staff Are Not Immediately Available C. Informed Consent D. Physical Environment E. Communication and Collaboration with the Patient's Treatment Team F. Emergency Management 1. Education and Training 2. Jurisdictional Mental Health Involuntary Hospitalization Laws 3. Patient Safety When Providing Services in a Setting with Immediately Available Professionals 4. Patient Safety When Providing Services in a Setting Without Immediately Available Professional Staff 5. Patient Support Person and Uncooperative Patients 6. Transportation 7. Local Emergency Personnel G. Medical Issues H. Referral Resources I .Community and Cultural Competency TECHNICAL GUIDELINES A. Videoconferencing Applications B. Device Characteristics C. Connectivity D. Privacy ADMINISTRATIVE GUIDELINES A. Qualification and Training of Professionals B. Documentation and Record Keeping C. Payment and Billing REFERENCES.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.117 | 0.060 |
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".