The Effects of the Affordable Care Act on the Practice of Psychiatry
Bibliographic record
Abstract
Article Abstract Click to enlarge page The Affordable Care Act is going to impact the practice of psychiatry as the number of insured Americans increases. Insurance companies are now required to provide coverage for children and adults with pre-existing conditions, eliminate dollar limits for lifetime coverage, and provide free preventive services. The delivery of psychiatric care is shifting toward preventing illness and creating patient-centered medical homes. Primary care physicians and specialists, such as psychiatrists, will function under new models that emphasize coordinated care teams and incorporate new technologies. Payments for physicians will be based on value rather than volume, and funding for research may include more partnerships to study new care delivery methods. As changes continue through 2014, clinicians must understand how their practice of psychiatry and patient care will be affected. From the Yale School of Medicine and the VA Connecticut Healthcare System, New Haven (Dr Ebert); Johns Hopkins Division of Child and Adolescent Psychiatry, Johns Hopkins Medicine, Baltimore, Maryland (Dr Findling); Penn State Hershey Milton S. Hershey Medical Center, Hershey, Pennsylvania (Dr Gelenberg); The Zucker Hillside Hospital, Glen Oaks; Hofstra North Shore-Long Island Jewish School of Medicine, Uniondale; and Behavior Health Services, North Shore-Long Island Jewish Health System, New Hyde Park, New York (Dr Kane); Bipolar Clinic and Research Program, Depression Clinical and Research Program, Harvard Medical School, and Massachusetts General Hospital, Boston (Dr Nierenberg); Banner Alzheimer's Institute, Alzheimer's Prevention Initiative, and the University of Arizona College of Medicine, Phoenix (Dr Tariot).†‹
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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 | 0.038 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".