Factors associated with attendance at primary care appointments after discharge from hospital: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Follow-up with a primary care provider within 1-2 weeks of discharge from hospital has been associated with reduced readmissions. We sought to determine appointment attendance with primary care providers postdischarge and identify factors associated with attendance. METHODS: We conducted a retrospective cohort study involving general medicine patients who had been discharged from hospital between Sept. 1, 2014, and Dec. 30, 2015, from 2 Ontario academic hospitals, and who had been supported by a transitional care specialist and advised to see a primary care provider within 1 week. Attendance was determined by self-report during follow-up by telephone. We used multivariable logistic regression to assess whether patient factors (e.g., comorbidity) or system factors (e.g., booking the appointment before discharge) predicted attendance. We used Cox proportional hazards modelling to assess whether attendance predicted readmission within 30 days. RESULTS: = 124) of patients attended an appointment within 2 weeks. After adjusting for age, sex and comorbidity, significant predictors of attendance were booking the appointment before discharge (odds ratio [OR] 2.14, 95% confidence interval [CI] 1.07-4.40), familiarity with the primary care provider (OR 5.43, 95% CI 2.25-14.1) and inclusion of a reminder, callback number and appointment time in the discharge summary (OR 15.3, 95% CI 2.09-326). Predictors of nonattendance were the presence of a home support worker (OR 0.38, 95% CI 0.17-0.80) and a booked specialist appointment before discharge (OR 0.37, 95% CI 0.18-0.73). Attendance was not associated with reduced readmissions (hazard ratio 0.66, 95% CI 0.40-1.09). INTERPRETATION: Timely follow-up with PCPs postdischarge remains challenging. Efforts to improve attendance should focus on reinforcing need for follow-up and coordinating follow-up before discharge, particularly for those poorly connected with the health care system.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".