Transitions of care for hospital discharges in a primary care network
Bibliographic record
Abstract
Transitions to and from primary care are a time of concern, especially for patients with chronic conditions and complex care needs. The Edmonton Southside Primary Care Network (ESPCN) developed a process for nurses to ensure timely post-discharge follow-up calls and physician appointments after hospitalization, assessing readmission risk with LACE and Clinical Frailty scores. Over 84% of eligible high-risk discharges received follow-up within 14 days. Of 7,400 index discharges, 1,464 had an emergency department revisit and 725 patients were readmitted within 30 days. Overall, ESPCN rates of readmission (9.8%) and rates of Family Practice Sensitive Conditions (FPSC) (5.7%) were significantly lower than national and provincial rates. FPSC rates for high-risk patients were significantly lower than low- or medium-risk groups. Consistent processes that support nursing involvement enable primary care teams to focus on those with highest risk for adverse outcomes and support patients to access the most appropriate place for the care they need.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".