Patient health engagement as a predictor of symptom outcomes in patients receiving chemotherapy
Bibliographic record
Abstract
PURPOSE: Patient health engagement (PHE), reflecting psychological readiness to participate in care, may influence how patients cope with symptoms. This study explored the associations between PHE and symptom outcomes in cancer patients receiving outpatient chemotherapy. METHODS: A secondary analysis of baseline data was conducted among 79 patients with gastrointestinal or hematological cancers. PHE was assessed using the Patient Health Engagement Scale (PHE-s; range 1-4). Symptom intensity was measured using the Edmonton Symptom Assessment Scale-revised (ESASr), and daily life interference with the MD Anderson Symptom Inventory-Interference scale (MDASI). Multivariate regression models tested the predictive value of PHE, adjusting for psychosocial covariates, age, and cancer type. RESULTS: The mean PHE score was 3.1 (SD = 0.6; range 1.6-4.0), with one-third of patients scoring below 3, indicating low engagement. Higher PHE predicted lower overall symptom intensity (β = -0.42, p < 0.001), emotional symptoms (β = -0.56, p < 0.001), and interference in daily life (β = -0.46, p < 0.001). Although PHE was associated with physical symptom intensity (β = -0.29, p = 0.024), the overall model was not significant, and ROC analysis showed poor discrimination. ROC analyses identified domain-specific PHE cut-off scores (≤ 2.3-2.9) that distinguished between low and high symptom burden for total, emotional, and interference outcomes. CONCLUSION: PHE, particularly psychological readiness, was associated with better overall, emotional, and functional symptom outcomes. Identifying patients with low engagement may enable more timely, tailored supportive care. Future research should validate these findings in larger and more diverse groups.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".