Exploring the Challenges of Implementing the Edmonton Symptom Assessment Scale in a Specialist Palliative Care Unit
Bibliographic record
Abstract
Background: Many symptom assessment tools have been developed to aid evaluation of patient’s symptoms. The Edmonton Symptom Assessment Scale is one such tool. The ESAS was introduced on the inpatient unit in Milford Hospice (a 30 bedded tertiary palliative care unit) in December 2007. However, a 3-month chart review revealed a low completion rate (20%) of the ESAS. Aim: The aim of this study was to assess the reasons for the low completion rate of the ESAS in the unit. Methods: A mixed methods approach using both questionnaire and focus group was undertaken. The population sampled was the nursing staff who were responsible for ensuring the completion of the ESAS in the unit on a daily basis. Results: The main reason for the low completion rate of the ESAS was that nursing staff perceived that it was too burdensome for sick patients to complete (76%). Also, nursing staff felt that the tool was not clinically helpful and that it was too time consuming for patients to regularly complete. Other important issues relating to the introduction process for symptom assessment tools are also identified. Conclusions: The results of this study are consistent with findings in the literature relating to other symptom assessment tools. Implementing such tools may be burdensome for patients with a poor functional status in an advanced cancer setting. Areas of focus for further research include shorter symptom assessment tools which are more reflective of a patients twenty four hour symptom profile, and also proxy rated assessment tools.
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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 | 0.046 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".