Increasing the Frequency and Timeliness of Pain Assessment and Management in Long-Term Care: Knowledge Transfer and Sustained Implementation
Bibliographic record
Abstract
Background. Although feasible protocols for pain assessment and management in long-term care (LTC) have been developed, these have not been implemented on a large-scale basis. Objective. To implement a program of regular pain assessment in two LTC facilities, using implementation science principles, and to evaluate the process and success of doing so. Methods. The implementation protocol included a pain assessment workshop and the establishment of a nurse Pain Champion. Quality indicators were tracked before and after implementation. Focus groups and interviews with staff were also conducted. Results. The implementation effort was successful in increasing and regularizing pain assessments. This was sustained during the follow-up period. Staff members reported enthusiasm about the protocol at baseline and positive results following its implementation. Despite the success in increasing assessments, we did not identify changes in the percentages of patients reported as having moderate-to-severe pain. Discussion. It is our hope that our feasibility demonstration will encourage more facilities to improve their pain assessment/management practices. Conclusions. It is feasible to implement regular and systematic pain assessment in LTC. Future research should focus on ensuring effective clinical practices in response to assessment results, and determination of longer-term sustainability.
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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 | 0.070 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".