Auditing Palliative Care Provided by Nurses for Chronic Pain Management in the Elderly
Bibliographic record
Abstract
Abstract Introduction : Pain is the most common mental pressure in the elderly and its abstract nature makes it a challenging subject to study. Conformity of palliative care management was examined with standards. Methods : Through a descriptive audit study, 210 elderly patients with chronic pain, who were candidates for palliative and curative care, were examined. A researcher-designed checklist of standard health care for pain management and McGill pain questionnaire were used for data gathering. Data analyses were performed using descriptive statistics and estimating conformity of the pain management measures with standards of SPSS (18). Results : Checking records of painkillers (60%) and reporting the patient’s pain to the physician (74.8%) were the most efficient palliative and curative measures, respectively. Surveying pain (41.9%) and introducing oneself to the patient (42.4%) were the least efficient healthcare services. In addition, palliative measures (24.73%) and drug-intervention measures (30.93%) had little conformity with the pain management standards. Conclusions: Pain management care provided for the elderly has a long way to meet standards. This notable difference can be rooted in the abstract nature of pain and lack of knowledge of the medical team about palliative and curative measures for pain management
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".