Multidisciplinary Health Care Professionals' Perceptions of the Use and Utility of a Symptom Assessment System for Oncology Patients
Bibliographic record
Abstract
PURPOSE: Despite growing implementation of electronic symptom assessment in oncology settings, few studies have described how standardized symptom assessment can enhance multidisciplinary care. The Edmonton Symptom Assessment System (ESAS) is a validated measure of symptom burden that has been adopted by Ontario's cancer centers to assess symptoms for patients with cancer. This study examines the perceived value of the ESAS among clinical teams and barriers to its use in enhancing multidisciplinary care. METHODS: Self-completed surveys were administered online to clinical teams at various disease-site clinics at a cancer center in Ontario, Canada. RESULTS: One hundred twenty-eight nurses, oncology physicians, and allied health professions completed the survey. The majority of nurses (89%), physicians (55%), and other providers (57%) reported referring to ESAS in clinic either "always" or "most of the time." Many of those who either "never" or "rarely" looked at ESAS scores reported finding it more efficient to talk to the patient or do their own assessment to determine symptom issues. Although most of the nurses and allied health professions found the ESAS to enhance patient care, help patients to articulate their symptom issues, and facilitate follow-up with patients with past symptom issues, only approximately half of the physicians agreed with these statements. CONCLUSION: Variable adoption of the ESAS by physicians may limit its potential to improve both interprofessional communication and comprehensive symptom control. To encourage consistent use, a symptom assessment system needs to be complementary to the perceived roles of all multidisciplinary team members, including physicians.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".