Evidence-based Development and Initial Validation of the Pain Assessment Checklist for Seniors With Limited Ability to Communicate-II (PACSLAC-II)
Bibliographic record
Abstract
OBJECTIVES: Our goal was to develop and validate, based on theoretical and empirical knowledge, the Pain Assessment Checklist for Seniors with Limited Ability to Communicate (PACSLAC-II), a shorter tool that would improve on the PACSLAC, while addressing limitations of the original version. METHODS: The PACSLAC was revised based on the relevant clinical and theoretical literature. Psychometric properties and clinical utility of the resulting 31-item PACSLAC-II were examined. Specifically, the PACSLAC-II was used to assess pain based on video footage of long-term care (LTC) residents with dementia undergoing painful procedures as part of routine care. Its ability to discriminate pain from non-pain-related states was compared with that of preexisting pain assessment tools using archival data. A second phase involved the use of the PACSLAC and PACSLAC-II by LTC staff to solicit feedback from health care providers. Mixed-methods analysis of this feedback was conducted. RESULTS: The PACSLAC-II demonstrated satisfactory reliability, excellent validity, and ability to differentiate between pain and nonpain states. The PACSLAC-II also accounted for unique variance in differentiating between pain and nonpain states, even after controlling for the preexisting tools combined, including the PACSLAC. The PACSLAC-II was also preferred by many LTC nurses and care aides, because of its length and condensed nature, which was thought to facilitate documentation and greater efficiency in pain management. DISCUSSION: Findings indicate that the empirical and theoretically driven revisions to the PACSLAC led to improved ability to differentiate between pain and nonpain states, while retaining its clinical utility.
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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 | 0.101 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".