The Process and Results of Integrating Stroke Services in a Large Urban Setting
Bibliographic record
Abstract
BackgroundIntegrating health care services across regions to improve patient outcomes and experience is a complex process. The literature is clear that receiving stroke care on a designated stroke unit produces improved patient outcomes. St. Josephu2019s Healthcare Hamilton (SJHH) and Hamilton Health Sciences (HHS), along with the Central South Regional Stroke Network undertook the challenge of developing an integrated model of stroke care for the city of Hamilton in Ontario, Canada. This integration required the transfer of stroke patient volumes and the reallocation of resources from SJHH to HHS. The ultimate result of this integration work was improve access to best practice stroke unit care for every person with stroke throughout Hamilton.ObjectiveTo describe the integration process and results of the integration to inform similar integration efforts. MethodsIntegration Process Structure:u2022Steering Committee: oversee integration of stroke servicesu2022Volumes and Definition Subcommittee: define patient population u2022Finance Planning Subcommittee: determine transfer of funds u2022People Planning Subcommittee: assess impact to Human Resources u2022Communication Planning Subcommittee: develop and disseminate messages for internal and external stakeholdersu2022Processes Subcommittee: develop patient flow pathwayu2022Operations Subcommittee: develop and implement pre-launch education, finalize equipment transfers and establish model evaluation structure Evaluation Process:u2022Monthly Case Reviewsu2022Quarterly Data Reviewsu20226-month and 1-year fulsome model evaluationsResultsThe Integrated model of Stroke Care in Hamilton was launched on January 8, 2018. Early analysis of the model demonstrates appropriate utilization of patient pathways and increased access to stroke unit care. Fulsome data will be available later in 2018.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".