Pain and Cognitive Status among Nursing Home Residents in Canada
Bibliographic record
Abstract
BACKGROUND: There is little information available on the rates of pain in institutionalized elderly persons, and this is particularly true for Canada. OBJECTIVES: To provide information about the prevalence and clinical correlates of pain in a sample of Canadian nursing homes, to determine whether residents with cognitive impairment experience lower rates of health conditions associated with pain (eg, arthritis) than residents without cognitive impairment and to determine whether the associations (ie, odds ratios) for pain with such health conditions vary as a function of cognitive status. DESIGN: The study is based on a secondary analysis of data collected with the minimum data set (MDS 2.0). SETTING AND PARTICIPANTS: The study comprised 3195 nursing home residents in Ontario, Manitoba and Saskatchewan. SUBJECTS AND METHODS: All residents were assessed with the MDS 2.0 by trained clinicians (usually nurses). Pain was documented if it had occurred within the seven days before the assessment. Assessors were trained to look for overt signs of discomfort, such as wincing or verbalizations. Self-report ratings were obtained when possible. RESULTS: The overall prevalence of pain in this sample was 49.7%, and 23.7% of residents experienced pain daily. Persons with and persons without cognitive impairments did not differ with respect to the prevalence of conditions likely to cause pain and the associations of pain with such health conditions. Regional differences were found, with Ontario residents having a higher frequency and intensity of pain than their counterparts in Saskatchewan and Manitoba. This may be due, at least in part, to regional differences in nursing home admission criteria. CONCLUSIONS: The findings suggest that the prevalence of identified pain is lower among nursing home residents with higher levels of cognitive impairment. These results do not support the notion that this is a function of lower prevalence rates of pain-causing conditions in nursing home residents with dementia. Furthermore, the results do not support the view that residents with cognitive impairments are less sensitive to pain. This study highlights the need for more comprehensive tools to assess pain in persons with cognitive impairments. Nonetheless, the MDS may be a useful instrument for detecting pain in such populations, because it does not rely exclusively on self-report.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".