The use of the Pain Assessment Checklist for Seniors with Limited Ability to Communicate (PACSLAC) by caregivers in dementia care.
Bibliographic record
Abstract
AIM: Pain is often under-detected and under-treated in nonverbal patients with severe dementia. PACSLAC is a behavioural assessment tool designed to improve the detection of pain in severe dementia. Previous studies on PACSLAC were primarily with qualified nurses in Canada and The Netherlands. This pilot study is aimed to evaluate the inter-rater reliability of the PACSLAC when it is administrated by caregiver staff. METHOD: 50 patients from four dementia care facilities were included. For each patient, a PACSLAC rating was completed independently by a medical undergraduate researcher and a caregiver following the caregiver attended the patient's usual personal care with the researcher observing in close proximity. RESULTS: 36 (72%) were female and 14 (28%) were male. The mean age was 82.9 years (SD=7.2) and the mean MMSE score was 7.5 (SD=7.9). A total of 12 caregivers participated in the study. The total PACSLAC scores ranged from 1 to 22 with a mean of 5.7 (SD=4.0). The average percentage of agreement was 0.89 and the Pearson correlation coefficient was 0.83 (p<0.01) for the total PASCLAC scores rated by the researcher and the caregivers. CONCLUSION: This pilot study demonstrated PACSLAC has good inter-rater reliability when it is used by caregivers. We believe a baseline PACSLAC could be performed for each patient at the time of admission to a dementia care facility and re-administered on regular intervals to detect pain-related behaviour and to prompt earlier pain management. Future studies with larger samples and collaboration between different centres will be useful in providing normative PACSLAC values in New Zealand.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".