Heart rate variability in unstably housed Veterans with mental health conditions
Bibliographic record
Abstract
OBJECTIVE: Almost 10% of Veterans have experienced homelessness, which is associated with complex healthcare needs and high levels of physical and mental health comorbidities. Measures of autonomic nervous system dysregulation, including higher resting heart rate (HR) and lower time domain and higher frequency domain measures of heart rate variability (HRV), are associated with worse physical and mental health in Veteran and civilian populations. However, these associations have not been explored in unstably housed Veterans with mental health conditions. METHOD: 43 male Veterans who were homeless/at-risk and receiving residential mental health treatment completed measures of HRV, neuropsychological performance, neuropsychiatric symptoms, and functioning. Time-domain and frequency-domain measures of HRV were calculated. Correlations between neuropsychological performance, symptoms, and HRV measures were computed. Multiple regression was used to examine predictors of variance in HRV variables. RESULTS: = 0.39-0.50). Multiple linear regression revealed that symptom and neuropsychological variables explained 22-50% of the variance in HR and HRV. CONCLUSIONS: HRV in Veterans may serve as a noninvasive biomarker correlate of healthcare needs in unstably housed Veterans.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".