Bibliographic record
Abstract
Emergency Strategic Clinical NetworkTM Quality and Innovation Forum Presentation Proposal Name: xx Position (e.g. patient care manager, professor): Manager Primary Affiliation: (AHS) Other: AHS Project Title: iOAT in the ED – Lessons Learned Hospital: All adult sites in Calgary Location: Calgary Team Members: xx & xx Background Deaths related to opioid poisoning have continued to climb over the last few years. The Injectable Opioid Agonist Treatment program (iOAT) provides injectable hydromorphone to those individuals with moderate to severe opioid use disorder and a history of injection drug use who have been unsuccessful with oral OAT and continue to be at high risk for opioid poisoning. Working with the emergency departments (ED) was identified as a critical step in the initial roll out of iOAT. Implementation The iOAT program began operating in October 2018. The clinic provides prescribed hydromorphone to clients within the program. Additionally, the team is comprised of physicians, nurse practitioners, nurses, social worker, peer support workers and administrative support to provide comprehensive wrap around care to every client that is registered to the program. It was recognized early on that the clients that were being served by iOAT were also high users of the ED and UCCs. Being part of iOAT became a factor that needed to be considered when these clients presented to the ED due to their prescription of hydromorphone. Working with management, medical leadership, and nurse educators, support and education were provided to ensure that iOAT clients were provided with optimal care when in the ED. Ongoing communication has been the primary strategy that has been used. Evaluation Methods The evaluation for this project has been informal and ongoing. The medical team at iOAT has worked with the medical team for the Calgary EDs to develop a detailed treatment plan that is visible on SCM. Telephone and emails have been the primary mode of feedback for both parties, and the plan is adjusted as necessary along the way. Results Improving the knowledge and understanding for all staff involved to understand iOAT and the role of the ED has been demonstrated to be effective when clients stay in the ED and don’t leave against medical advice, which likely occurred before. Additionally, the trust that is built within the iOAT clinic is maintained when the ED is a partner in care and as appropriate, provides them with the dosing that they would normally receive at iOAT. Advice and Lessons Learned 1) Involve the emergency department management in planning or initial implementation 2) Communicate, Communicate, Communicate 3) Use continuous feedback to adjust to find the best strategies to provide patient care
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".