Oncology Nurses’ Attitudes Toward the Edmonton Symptom Assessment System: Results From a Large Cancer Care Ontario Study
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To examine oncology nurses' attitudes toward and reported use of the Edmonton Symptom Assessment System (ESAS) and to determine whether the length of work experience and presence of oncology certification are associated with their attitudes and reported usage. . DESIGN: Exploratory, mixed-methods study employing a questionnaire approach. . SETTING: 14 regional cancer centers (RCCs) in Ontario, Canada. . SAMPLE: Oncology nurses who took part in a larger province-wide study that surveyed 960 interdisciplinary providers in oncology care settings at all of Ontario's 14 RCCs. . METHODS: Oncology nurses' attitudes and use of ESAS were measured using a 21-item investigator-developed questionnaire. Descriptive statistics and Kendall's tau-b or tau-c test were used for data analyses. Qualitative responses were analyzed using content analysis. . MAIN RESEARCH VARIABLES: Attitudes toward and self-reported use of standardized symptom screening and ESAS. . FINDINGS: More than half of the participants agreed that ESAS improves symptom screening, most said they would encourage their patients to complete ESAS, and most felt that managing symptoms is within their scope of practice and clinical responsibilities. Qualitative comments provided additional information elucidating the quantitative responses. Statistical analyses revealed that oncology nurses who have 10 years or less of work experience were more likely to agree that the use of standardized, valid instruments to screen for and assess symptoms should be considered best practice, ESAS improves symptom screening, and ESAS enables them to better manage patients' symptoms. No statistically significant difference was found between oncology-certified RNs and noncertified RNs on attitudes or reported use of ESAS. . CONCLUSIONS: Implementing a population-based symptom screening approach is a major undertaking. The current study found that oncology nurses recognize the value of standardized screening, as demonstrated by their attitudes toward ESAS. . IMPLICATIONS FOR NURSING: Oncology nurses are integral to providing high-quality person-centered care. Using standardized approaches that enable patients to self-report symptoms and understanding barriers and enablers to optimal use of patient-reported outcome tools can improve the quality of patient care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".