Improving Cardiometabolic Health in Individuals Taking Antipsychotic Drugs at Burnaby Primary Care Clinic
Bibliographic record
Abstract
Abstract Metabolic syndrome is a common health issue in individuals with mental health diagnoses and taking antipsychotic drugs. In this Doctor of Nursing Practice (DNP) pilot project, a nurse practitioner (NP) at mental health specialty primary care clinic in British Columbia, Canada, implemented an eight-week evidence-based program to motivate clients to initiate healthy behaviors. The project set the PICO question as "Do individuals with mental health illness being treated with antipsychotic drugs (and receiving treatment via telehealth visits) (P) who perform regular self-abdominal circumferences measurement and receive patient education about risks for metabolic syndrome (I) initiate more lifestyle-changing behaviors (O) than prior to these interventions? (C)". The project recruited five mentally and physically stable participants receiving antipsychotic drugs associated with metabolic syndrome from the clinic. All the participants received education on the risks of metabolic syndrome and healthy behaviors from the NP via telephone. The participants were also encouraged to measure their abdominal girth and followed up every two weeks, up to eight weeks. Additionally, health-related quality of questionnaires (HRQOL) were administered at weeks one and eight to see if their health perception improved. Although HRQOL scores and abdominal circumference measurements did not change with statistical significance, the mean of abdominal circumference measurements declined at week eight. Furthermore, the participants who completed the program, initiated and maintained healthy behaviors in week eight. Although the results were limited to this clinic, this project suggests a potential for the future application of such a cardiometabolic program in the clinics in a similar setting in the region. Keywords: DNP Project, Cardiometanolic, Metabolic Syndrome, Mental Health, Primary Care
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".