The Basics for Psychiatrists: An Overview of Sleep, Sleep Disorders, and Psychiatric Medications’ Effects on Sleep
Bibliographic record
Abstract
<P>Recognition and assessment of a patient’s sleep pattern and sleep problems have often been cited as lacking in the evaluation of patients who present for treatment of mental health conditions and/or substance use disorders. The reason for this, in part, relates to the complex interrelationship between sleep, psychiatric illness, and psychotropic medications. There is a challenge for physicians in evaluating sleep in individuals who have either substance-use disorders or psychiatric disorders because these areas are often interrelated. It is well known that psychiatric illness of various types will cause sleep disturbances. </P> <h4>ABOUT THE AUTHORS</h4> <P>R. Jeffrey Goldsmith, MD, DFAPA, is a Psychiatrist with the VA Medical Center, Veterans Affairs Medical Center, Dual Diagnosis Services, and Professor of Clinical Psychiatry, Department of Psychiatry, University of Cincinnati. Paul G. Casola, MD, PhD, FRCPC, is a Psychiatrist with the Salvation Army Harbour Light Centre, and Lecturer, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.</P> <P>Address correspondence to: R. Jeffrey Goldsmith, MD, DFAPA, 3200 Vine Street, Cincinnati, OH 45220; fax 513-487-6046; or e-mail <a href="mailto:Jeffrey.Goldsmith@va.gov">Jeffrey.Goldsmith@va.gov</a>. </P> <P>The authors disclosed no relevant financial relationships.</P>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".