Understanding Needs, Interactions, Treatment, and Expectations Among Individuals Affected by Bipolar Disorder or Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: The overarching aim of the Understanding Patients' Needs, Interactions, Treatment, and Expectations (UNITE) Global Survey was to empirically evaluate the attitudes and general fund of knowledge that individuals with bipolar disorder or schizophrenia possess regarding somatic health issues. METHOD: The UNITE survey was an Internet-based initiative that recruited patients and caregivers from 11 countries (Australia, Brazil, France, Greece, Germany, Italy, Spain, South Korea, Sweden, the United States, and the United Kingdom). Opinion Research Corporation, Princeton, NJ, conducted the UNITE survey between June 10, 2006, and September 11, 2006, and analyzed the results. RESULTS: A total of 5,074 respondents participated in the survey. From this total sample, 1,155 individuals with schizophrenia and 1,300 with bipolar disorder were self-identified. Psychiatrists were identified as the individuals primarily responsible for patients' medication prescription and surveillance of both psychological and physical health. The majority of respondents in both groups had been receiving medication for more than 5 years. Weight gain was the most commonly cited adverse event associated with medication use. Moreover, weight gain was identified as a contributing factor to general medical comorbidity (eg, diabetes mellitus) and as a detractor to quality of life. Most respondents identified weight gain and general physical health as areas of deficiency in their perceived knowledge and interactions with health care providers. Notwithstanding the ubiquity and implications of comorbid medical disorders and medication-related adverse events, most respondents did not receive opportunistic screening or surveillance for medical risk factors and disorders. Overall, respondents expressed general dissatisfaction when interacting with mental health care providers. CONCLUSION: Metabolic consequences of psychotropic medication are the most concerning aspect of medication treatment for patients, contributing to perceived morbidity, quality-of-life reduction, and reduced satisfaction with care. Despite the compelling database that underscores the hazards attributable to comorbid general medical conditions, most individuals with schizophrenia and bipolar disorder receive guideline-discordant care for somatic health conditions as well as for the principal psychiatric disorder. Barriers to somatic health care access for those with schizophrenia and bipolar disorder, as well as the impact of targeted interventions, warrant future research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".