Impact of implementing the pain best practice guideline in a long-term care home using the knowledge-to-action framework
Bibliographic record
Abstract
Introduction. Assessment and management of pain in older adults can be challenging, with persistent pain prevalence ranging from 25% to 80%, especially in long term care homes (LTCH), where most seniors are unable to verbalize their pain. This article describes the implementation of the Registered Nurses’ Association of Ontario (RNAO) Assessment and Management of Pain (Third Edition) best practice guideline (BPG) in a LTCH in Toronto, Canada. Methodology. Using mixed descriptive study methodology, this 391-bed home housing older adults over 80 years implemented the Pain guideline using the knowledge-to-action framework and audit procedures to evaluate the impact of implementing this guideline. Key implementation activities included educating residents, families, and staff about pain while integrating validated pain screening and assessment tools into practice. A mixed methods approach of qualitative and quantitative data was utilized to monitor improvements in clinical and organizational outcomes. Results. The impact of implementing Pain BPG is: improved utilization of pain assessment and management tools, reduced incidence of worsening pain, improved pain scores and improved resident quality of life. The structured and integrated, evidence-based approaches to pain assessment and management reflected that long-term care residents don’t have to live with pain regularly, which leads to a better quality of life and resident/family satisfaction. Discussion. In conjunction with the structured approach of the knowledge-to-action framework and the Pain BPG, the LTCH utilized tailored approaches to meet the needs of their resident population. Recognizing the unique needs of seniors in a residential setting, organizational structural facilitators, and barriers and cultural needs, the LTCH developed multi modal approaches based on a person and family centred approach. This evidence-based and resident focused approach was the key to the successful implementation and subsequent outcomes that were resultant. Conclusion. The systematic implementation of the RNAO pain BPG and the utilization of the knowledge-to-action framework was shown to improve resident outcomes, improve organizational processes and generate staff satisfaction. Participation and engagement of residents, their families and health professionals in the process was one of the greatest facilitators.
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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 | 0.036 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".