Differences in Symptom Distress Based on Gender and Palliative Care Designation Among Hospitalized Patients
Bibliographic record
Abstract
PURPOSE: To explore patient-reported symptom distress in relation to documentation of symptoms and palliative care designation in hospital inpatients. DESIGN: This cross-sectional study analyzed data from 710 inpatients at two large hospitals in Sweden using the Edmonton Symptom Assessment Scale and the Memorial Symptom Assessment Scale. Chart reviews focused on nurses' and physicians' symptom documentation and palliative turning point. METHODS: Descriptive statistics were calculated for all variables and provided summaries about the sample. Patients were grouped according to gender, age, palliative care designation, and symptom documentation. The t test and chi-square test were used to calculate whether symptom distress varied between groups. A two-way analysis of variance was conducted for multiple comparisons to explore the impact of gender and age on mean symptom distress. FINDINGS: Females reported higher levels of symptom distress than did males related to pain, fatigue, and nausea. When comparing symptom distress between males and females with documentation pertaining to symptoms, there were significant differences implying that females had to report higher levels of symptom distress than males in order to have their symptoms documented. CONCLUSIONS: Females need to report higher levels of symptom distress than do males for healthcare professionals to identify and document their symptoms. It can be hypothesized that females are not receiving the same attention and symptom alleviation as men. If so, this highlights a serious inequality in care that requires further exploration. CLINICAL RELEVANCE: Considering that common reasons why people seek health care are troublesome symptoms of illness, and that the clinical and demographic characteristics of inpatients are changing towards more advanced ages with serious illnesses, inadequate symptom assessment and management are a serious threat to the care quality.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".