A Controlled Investigation of Continuing Pain Education for Long‐Term Care Staff
Bibliographic record
Abstract
BACKGROUND: The underassessment and undertreatment of pain in residents of long-term care (LTC) facilities has been well documented. Gaps in staff knowledge and inaccurate beliefs have been identified as contributors. OBJECTIVES: To investigate the effectiveness of an expert-based continuing education program in pain assessment⁄management for LTC staff. METHODS: Participants included 131 LTC staff members who were randomly assigned to either an interactive pain education (PE) program, which addressed gaps in knowledge such as medication management, or an interactive control program consisting of general dementia education without a specific clinical focus. Participants attended three sessions, each lasting 3 h, and completed measures of pain-related knowledge and attitudes⁄beliefs before, immediately after and two weeks following the program. Focus groups were conducted with a subset of participants to gauge perception of the training program and barriers to implementing pain-related strategies. RESULTS: Analysis using ANOVA revealed that PE participants demonstrated larger gains compared with control participants with regard to pain knowledge and pain beliefs. Barriers to implementing pain-related strategies certainly exist. Nonetheless, qualitative analyses demonstrated that PE participants reported that they overcame many of these barriers and used pain management strategies four times more frequently than control participants. CONCLUSIONS: Contrary to previous research, the present study found that the interactive PE program was effective in changing pain beliefs and improving knowledge. Continuing PE in LTC has the potential to address knowledge gaps among front-line LTC providers.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".