Knowledge translation: An interprofessional approach to integrating a pain consult team within an acute care unit
Bibliographic record
Abstract
Management of pain in the frail elderly presents many challenges in both assessment and treatment, due to the presence of multiple co-morbidities, polypharmacy, and cognitive impairment. At Baycrest Health Sciences, a geriatric care centre, pain in its acute care unit had been managed through consultations with the pain team on a case-by-case basis. In an intervention informed by knowledge translation (KT), the pain specialists integrated within the social network of the acute care team for 6 months to disseminate their expertise. A survey was administered to staff on the unit before and after the intervention of the pain team to understand staff perceptions of pain management. Pre- and post-comparisons of the survey responses were analysed by using t-tests. This study provided some evidence for the success of this interprofessional education initiative through changes in staff confidence with respect to pain management. It also showed that embedding the pain team into the acute care team supported the KT process as an effective method of interprofessional team building. Incorporating the pain team into the acute care unit to provide training and ongoing decision support was a feasible strategy for KT and could be replicated in other clinical settings.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".