ASSESSMENT AND MANAGEMENT OF CHRONIC PAIN IN THE OLDER PERSON LIVING IN THE COMMUNITY
Bibliographic record
Abstract
AIM: This paper reviews the nursing research literature on chronic pain in the older person living in the community and suggests areas for future research. BACKGROUND: Chronic pain is a pervasive and complex problem that is difficult to treat appropriately. Nurses managing chronic pain in older people in domiciliary/home/community nursing settings face many challenges. To provide care, the many parameters of chronic pain which include the physical as well as the psycho-social impact and the effect of pain on patients and their families, must be carefully assessed. Beliefs of the older person about pain and pain management are also important. METHOD: Relevant nursing studies were searched using CINAHL, Cochrane Database of Systematic Reviews, EMBASE and PUBMED databases using key words about pain and the older person that were appropriate to each database. RESULTS: Tools to assess pain intensity in the older person have been studied but there has been less research on the other parameters of pain assessment or how the older person manages pain. An effective nurse-patient relationship is an important component of this process and one that needs more study. Few research studies have focused on how nurses can be assisted, or on the challenges, nurses' face, when managing this vulnerable population. CONCLUSION: A broad approach at the organisational level will assist nurses to manage this health care issue.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".