Symptom Experiences: Perceptual Accuracy Between Advanced-Stage Cancer Patients and Family Caregivers in the Home Care Setting
Bibliographic record
Abstract
PURPOSE: This study used a comparative descriptive design to compare family caregivers' and advanced-stage cancer patients' perceptions of patients' multidimensional symptom experiences on presence, frequency, severity, and distress. PATIENTS AND METHODS: A convenience sample of 98 dyads, composed of advanced-stage heterogeneous cancer patients and their caregivers, completed the Memorial Symptom Assessment Scale in the home care setting on a one-time basis. This scale is a 32-item Likert-type scale for assessing the presence, frequency, severity, and distress arising from symptoms in cancer patients. RESULTS: There was confirmation of trends previously described in related studies where, for example, caregivers tend to overreport on symptom experiences. However, the degree of absolute difference between patient and caregiver responses was normally around 1 unit (on a theoretical range of 0 to 4 units). Levels of patient-caregiver agreement were better on more concrete questions related to symptom frequency, severity, and distress than on broad questions related to the presence of a symptom. Patients and caregivers achieved better levels of agreement on physical versus psychological symptoms. CONCLUSION: The findings indicated that family caregivers can provide reasonable proxy or complementary reports on patient symptom experiences of frequency, severity, and distress. However, family caregivers have greater difficulty in achieving high levels of accuracy on psychological versus physical symptoms.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".