Reasons for Returning to the Emergency Department: Perspectives of Patients and the Liaison Nurse Clinician
Bibliographic record
Abstract
Objectives: The authors wanted to understand the reasons why patients discharged from internal medicine units return to the emergency department within a short term period. The purpose of the study was to explore patients’ perspective of their reasons for returning to the emergency department within fourteen days post-discharge from an internal medicine unit, and to examine how these reasons relate to those determined by the liaison nurse clinician prior to discharge.Methods: A qualitative descriptive design was selected to develop the study and individual face-to-face semi-structured interviews were conducted with participants. A convenience sample of eight participants was recruited from a major teaching hospital in Montreal, Canada. The study triangulated three different data sources, which were the patient’s perspective through the interview and the liaison nurse clinician’s perspective through the use of two evaluation tools, which were the Bounceback Probability Legend and the LACE Index Scoring Tool.Results: The participants attributed their return to the emergency department due to 1) being discharged too soon, 2) feeling weak at discharge, 3) having limited discharge instructions prior to discharge, and 4) having limited resources available to rely on for help once home. It was also noticed that participants went through a decision-making process for choosing to return to the emergency department. Additionally, the liaison nurse clinician’s evaluation tools identified different reasons from those the participants had attributed to their return to the emergency department.Conclusions: The findings suggest that health care professionals must evaluate and assess patients on several components upon their discharge, such as the understanding of their illness, primary concerns, and readiness prior to discharge. The study provides further data in supporting the need of patient’s involvement in the process of discharge planning.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".