Educational Needs of Health Care Providers Working in Long‐Term Care Facilities with Regard to Pain Management
Bibliographic record
Abstract
BACKGROUND: The prevalence of chronic pain ranges from 40% to 80% in long-term care facilities (LTCF), with the highest proportion being found among older adults and residents with dementia. Unfortunately, pain in older adults is underdiagnosed, undertreated, inadequately treated or not treated at all. A solution to this problem would be to provide effective and innovative interdisciplinary continuing education to health care providers (HCPs). OBJECTIVE: To identify the educational needs of HCPs working in LTCF with regard to pain management. METHODS: A qualitative research design using the nominal group technique was undertaken. Seventy-two HCPs (21 physicians⁄pharmacists, 15 occupational⁄physical therapists, 24 nurses and 21 orderlies) were recruited from three LTCF in Quebec. Each participant was asked to provide and prioritize a list of the most important topics to be addressed within a continuing education program on chronic pain management in LTCF. RESULTS: Forty topics were generated across all groups, and six specific topics were common to at least three out of the four HCP groups. Educational need in pain assessment was ranked the highest by all groups. Other highly rated topics included pharmacological treatment of pain, pain neurophysiology, nonpharmacological treatments and how to distinguish pain expression from other behaviours. CONCLUSION: The present study showed that despite an average of more than 10 years of work experience in LTCF, HCPs have significant educational needs in pain management, especially pain assessment. These results will help in the development of a comprehensive pain management educational program for HCPs in LTCF.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".