Pain in Canadian Long-Term Care Homes: A Call for Action
Bibliographic record
Abstract
Navigating the evaluation and management of pain in long-term care homes is a complex task. Despite an extensive body of literature advocating for a paradigm shift in pain assessment and management within long-term care homes, much more remains to be done. The assessment of pain in long-term care is particularly challenging, given that a substantial proportion of residents live with some degree of cognitive impairment. Individuals living with dementia may encounter difficulties articulating the frequency and intensity of their pain, potentially resulting in an underestimation of their pain. In Canada and in the United States, the interRAI Minimum Data Set 2.0, Minimum Data Set 3.0, and the interRAI Long-Term Care Facilities assessments are administered to capture the presence and intensity of pain. These assessment instruments are used both on admission and quarterly, offering a reliable and validated method for comprehensive assessment. Nonetheless, the daily assessment and documentation of pain across long-term care homes, which is used to inform the interRAI Pain Scale, is not always consistent. The reality is that assessing pain can be inaccurate for several reasons, including the fact that it is rated by long-term care staff with diverse levels of expertise, resources, and education. This call for action explores the current approaches used in pain assessment and management within long-term care homes. The authors not only bring attention to the existing challenges but also emphasize the necessity of considering a more comprehensive assessment approach.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 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".