Practice Guidelines for Assessing Pain in Older Persons with Dementia Residing in Long-Term Care Facilities
Bibliographic record
Abstract
PURPOSE: Frail patients with dementia most frequently present with musculoskeletal pain and mobility concerns; therefore, physiotherapy interventions for this population are likely to be of great benefit. However, physiotherapists who work with older adults with dementia confront a considerable challenge: the communication impairments that characterize dementia make it difficult to assess pain and determine its source. For an effective physiotherapy programme to be implemented, valid pain assessment is necessary. This paper is intended to provide practice guidelines for pain assessment among older persons with dementia. SUMMARY OF KEY POINTS: Over the last several years, there has been tremendous research progress in this area. While more research is needed, several promising assessment methodologies are available. These methodologies most often involve the use of observational checklists to record specific pain behaviours. RECOMMENDATIONS: We encourage the ongoing and regular evidence-based pain assessment of older persons with dementia, using standardized procedures. Without regular and systematic assessment, pain problems will often go undetected in this population. Given the need for systematic pain assessment and intervention for long-term care populations with mobility concerns and muculoskeletal pain problems, we call for increased involvement of physical therapists in long-term care facilities.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".