Pain in Dementia: Use of Observational Pain Assessment Tools by People Who Are Not Health Professionals
Bibliographic record
Abstract
OBJECTIVE: Pain is prevalent among older adults but is often underestimated and undertreated, especially in people with severe dementia who have limited ability to self-report pain. Pain in patients with moderate to severe dementia can be assessed using observational tools. Informal caregivers (relatives of seniors with dementia) are an untapped assessor group who often bear the responsibility of care for their loved ones. Our objective was to evaluate the ability of laypeople to assess pain using observational measures originally developed for use by health care professionals. DESIGN: We employed a quasi-experimental design and presented videos depicting patients with dementia (portrayed by actors) displaying pain behaviors or during a calm relaxed state (no pain) to long-term care nurses and laypeople. Participants rated the pain behaviors observed in each video by completing two standardized observational measures that had been previously developed for use by long-term care staff. RESULTS: As expected, both laypeople and nurses were able to effectively differentiate painful from nonpainful situations using the standardized tools. Both groups were also able to discriminate among gradations of pain (i.e., no pain, mild, moderate, severe) and required comparable amounts of time to complete the assessments. CONCLUSIONS: We conclude that, as hypothesized, the instruments under study can be used for the assessment of pain by laypeople. This is the first study to validate these instruments for use by laypeople. The use of these tools by laypeople (under the guidance of health professionals) has the potential of facilitating earlier detection and treatment of pain in older adults with dementia who live in community settings.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".