Care Transitions: Using Narratives to Assess Continuity of Care Provided to Older Patients after Hospital Discharge
Bibliographic record
Abstract
BACKGROUND: A common scenario that may pose challenges to primary care providers is when an older patient has been discharged from hospital. The aim of this pilot project is to examine the experiences of patients' admission to hospital through to discharge back home, using analysis of patient narratives to inform the strengths and weaknesses of the process. METHODS: For this qualitative study, we interviewed eight subjects from the Sheldon M. Chumir Central Teaching Clinic (CTC). Interviews were analyzed for recurring themes and phenomena. Two physicians and two resident learners employed at the CTC were recruited as a focus group to review the narrative transcripts. RESULTS: Narratives generally demonstrated moderate satisfaction among interviewees with respect to their hospitalization and follow-up care in the community. However, the residual effects of their hospitalization surprised five patients, and five were uncertain about their post-discharge management plan. CONCLUSION: Both secondary and primary care providers can improve on communicating the likely course of recovery and follow-up plans to patients at the time of hospital discharge. Our findings add to the growing body of research advocating for the implementation of quality improvement measures to standardize the discharge process.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".