A Novel Approach to Measurement-Based Care: Integrating Palliative Care Tools Into Geriatric Mental Health
Bibliographic record
Abstract
Background: Older adults cared for in a geriatric mental health program often have medical co-morbidities causing physical symptoms which may be under-recognized. We explore the utility of palliative care tools in this patient population to identify the burden of symptoms and impact on patient dignity. Methods: Participants were recruited from a geriatric mental health inpatient unit and outpatient day hospital. Mood and somatic symptoms were tracked with self-report rating scales, including the Geriatric Depression Scale (GDS) and the Geriatric Anxiety Inventory (GAI) used in psychiatry, as well as the Edmonton Symptom Assessment Scale (ESAS) and Patient Dignity Inventory (PDI) used in palliative care. Demographic characteristics were collected from a retrospective chart review. Exploratory longitudinal models were developed for the GDS and GAI outcomes to assess change over time after adjusting for ESAS and PDI item scores. Results: Data were obtained for 33 English speaking patients (inpatients N = 17, outpatients N = 16) with a mean age of 76.5 (SD = 6.1). At baseline, several ESAS symptom burdens were rated as moderate and the PDI often captured physically distressing symptoms. GDS scores declined over time but at a slower rate for those reporting higher levels of pain on the ESAS ( P = .04). GAI scores declined over time but at a slower rate for those identifying physically distressing symptoms on the PDI ( P = .04). Conclusions: This study demonstrates how using the ESAS and PDI in a mental health population can be helpful in tracking symptoms and how these symptoms are related to psychiatric outcomes.
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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 | 0.065 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".