Expert consensus statement for telepsychiatry and attention-deficit hyperactivity disorder
Bibliographic record
Abstract
Changing practice patterns caused by the pandemic have created an urgent need for guidance in prescribing stimulants using telepsychiatry for attention-deficit hyperactivity disorder (ADHD). A notable spike in the prescribing of stimulants accompanied the suspension of the Ryan Haight Act, allowing the prescribing of stimulants without a face-to-face meeting. Competing forces both for and against prescribing ADHD stimulants by telepsychiatry have emerged, requiring guidelines to balance these factors. On the one hand, factors weighing in favor of increasing the availability of treatment for ADHD via telepsychiatry include enhanced access to care, reduction in the large number of untreated cases, and prevention of the known adverse outcomes of untreated ADHD. On the other hand, factors in favor of limiting telepsychiatry for ADHD include mitigating the possibility of exploiting telepsychiatry for profit or for misuse, abuse, and diversion of stimulants. This Expert Consensus Group has developed numerous specific guidelines and advocates for some flexibility in allowing telepsychiatry evaluations and treatment without an in-person evaluation to continue. These guidelines also recognize the need to give greater scrutiny to certain subpopulations, such as young adults without a prior diagnosis or treatment of ADHD who request immediate-release stimulants, which should increase the suspicion of possible medication diversion, misuse, or abuse. In such cases, nonstimulants, controlled-release stimulants, or psychosocial interventions should be prioritized. We encourage the use of outside informants to support the history, the use of rating scales, and having access to a hybrid model of both in-person and remote treatment.
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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 | 0.059 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.025 | 0.015 |
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".