Implementation of pain best practices as part of the spinal cord injury knowledge mobilization network
Bibliographic record
Abstract
Context/objective: The spinal cord injury (SCI) knowledge mobilization network (KMN) is a community of practice formed in 2011 as part of a national best practice implementation (BPI) effort to improve SCI care. This study objective was to determine whether completion and documentation of pain practices could be improved in a neurorehabilitation setting using the KMN implementation approach.Design: Single site, pre–post intervention study.Setting: Neurorehabilitation hospital.Participants: Twenty sequential consenting inpatients with SCI, with retrospective comparative analysis of 50 sequential SCI admissions pre-KMN.Interventions: A local Site Implementation Team (SIT) was formed to develop an implementation plan, including acceptable timeframes for completion and documentation of four specific pain best practices: (1) pain assessment on admission, (2) development of an Inter-Professional Pain Treatment Plan (IPTP), (3) pain monitoring throughout admission, and (4) a pain discharge plan.Outcomes: Provider adherences to pain best practices were the primary outcomes. The secondary outcome was patient satisfaction.Results: Provider adherence for most outcomes exceeded 70% completion within acceptable timeframes, with improvements found for all outcomes as compared to the retrospective cohort. Notably, pain education as part of the IPTP improved from 12% completion to 74%, documenting pain onset from 4.5% to 80% and pain discharge plan from 40% to 74%. Overall, participants were satisfied with their pain management.Conclusions: Pain best practices were more consistently documented after the KMN implementation. Pain practices in all four areas have now been expanded to all inpatient diagnoses using the same forms and framework created in the implementation process.
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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 | 0.074 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".