Improving the Physical Health of Psychiatric Hospital Residents: An Evaluation of an Obesity Education Program for Mental Health Professionals
Bibliographic record
Abstract
BACKGROUND: People living with mental health disorders are at increased risk for developing obesity due to poor diet, physical inactivity, and antipsychotic medications. In the United States, the obesity rate is 36% in the general population and more than 50% for people living with mental health disorders. Although mental health clinicians concentrate on managing psychiatric disorders, they seldom recognize the gradual increase in body mass index of their patients. The result is a disconnection between the clinical management of psychiatric disorders and the medical management of obesity. PURPOSE: This study assessed the effectiveness of an evidence-based education program for improving the obesity management practices of mental health clinicians caring for residents at a state psychiatric hospital. METHODS: This was a quasi-experimental study design with a pretest and posttest evaluation. Convenience sampling was used to recruit mental health professionals, or clinicians, at a large psychiatric hospital in the Southern region of the United States. Data was collected with the Advising and Treating Overweight and Obese Patient questionnaire (17 items). Data analysis included descriptive and inferential statistics. The findings were reported in accordance with the TREND and GREET guidelines. RESULTS: The education program was completed by 50 MHCs. The pretest indicated that 76% of MHCs were not involved in helping obese residents manage their weight, but the posttest indicated 90% were involved. There was a significant increase in MHC knowledge about obesity management and reported actions 90-days after the program. MHCs were unable to arrange follow-up visits for residents, a task not directly within their control. CONCLUSIONS: Mental health clinicians reported increased knowledge and improved clinical practice after an education program. Because the outcomes were reported at 90-days after the program, further research needs to evaluate the longitudinal impact of this type of program, where the reported behaviors are correlated to process and clinical outcome measures for obesity.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".