Pain Assessment as Intervention
Bibliographic record
Abstract
OBJECTIVES: The communication impairments that characterize severe dementia make pain assessment challenging. As such, pain problems often go undetected. Our goal was to determine whether systematic pain assessment leads to improved pain management practices and decreases nursing stress in comparison with a control condition. METHODS: We adopted a 3-month comparative longitudinal design. Nursing staff regularly assessed dementia patients' pain through the use of the Pain Assessment Checklist for Seniors with Limited Ability to Communicate (PACSLAC). A second group of nurses completed an attention-control measure for a control group of patients. In addition, nursing staff regularly completed measures of work stress to investigate the effects of the workload associated with systematic pain assessment on nurse stress. RESULTS: Regular use of the PACSLAC improved pain management practices over time as reflected in increased usage of analgesic medications (prescribed on "as needed" basis) in comparison with the control group. As pain interventions increased, a corresponding decrease in observable pain behaviors (as reflected on the PACSLAC assessments that were completed by the nurses) was observed. In addition, nurses who used the PACSLAC reported decreased distress and burnout over time. DISCUSSION: This investigation provides strong support for both the importance of systematic pain assessment in long-term care and for the clinical utility of the PACSLAC in improving pain management practices and decreasing caregiver distress.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.005 |
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".