Implementation of the pain and symptom assessment record (PSAR)
Bibliographic record
Abstract
BACKGROUND: Symptom control is a major component of care for the terminally ill patients. Although uncontrolled pain is distressing for patients and families, there are other symptoms that can be distressing such as dyspnea and fatigue. Determining methods to consistently assess and manage pain and other symptoms is a challenge for nurses, physicians and other health care professionals. In the Ottawa Region of Canada, health care providers raised concerns related to inconsistencies in pain assessment due to a variety of formats used, as the patient moved through the health care system. Recognizing the need for a common assessment tool, a working group was formed composed of 14 nurses associated with institutions and agencies delivering palliative care services in the Ottawa region, as well as a faculty member of the School of Nursing of the University of Ottawa. The mandate of the working group was to develop a consistent method to assess patients' pain and symptoms in order to facilitate communication among health care professionals within various health care settings. The Pain and Symptom Assessment Record (PSAR) was developed over 24 months. AIM: To determine the feasibility of implementing the PSAR in a variety of settings. METHODS: This exploratory study used focus groups and chart audits to gather data related to the utility of the PSAR. Education sessions were used to introduce the tool to nurses in the various settings. RESULTS: The tool was implemented in 12 settings. Thirty-seven education sessions were given to nurses prior to use of the tool and the feedback revealed that this is an important process in tool introduction. The results of the chart audits indicated that pain was assessed 93% of the time. Symptoms were less documented but fatigue was most prominent. Overall, patients were satisfied with their pain and symptom control. Data from the focus groups were analysed using content analysis and the two themes that emerged related to the tool were 'structure' and 'process'. CONCLUSION: There were many challenges in this project and lessons learned will be discussed. Based on the results, the tool has been modified and is currently utilized in diverse settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".