Developing an in-depth understanding of patient and caregiver engagement across care transitions from hospital: protocol for a qualitative study exploring experiences in Canada
Bibliographic record
Abstract
INTRODUCTION: Patient and caregiver engagement is critical, and often compromised, at points of transition between care settings, which are more common, and more challenging, for patients with complex medical problems. The consequences of poor care transitions are well-documented, both for patients and caregivers, and for the healthcare system. With an ageing population, there is greater need to focus on care transition experiences of older adults, who are often more medically complex, and more likely to require care from multiple providers across settings. The overall goal of this study is to understand what factors facilitate or hinder patient and caregiver engagement through transitions in care, and how these current engagement practices align with a previously developed engagement framework (CHOICE Framework). This study also aims to co-develop resources needed to support engagement and identify how these resources and materials should be implemented in practice. METHODS AND ANALYSIS: This study uses ethnographic approaches to explore the dynamics of patient and caregiver engagement, or lack thereof, during care transitions across three regions within Ontario. With the help of a front-line champion, patients (n=18-24), caregivers (n=18-24) and healthcare providers (n=36-54) are recruited from an acute care hospital unit (or similar) and followed through their care journey. Data are collected using in-depth semi-structured interviews. Workshops will be held to co-develop strategies and a plan for future implementation of resources and materials. Analysis of the data will use inductive and deductive coding techniques. ETHICS AND DISSEMINATION: Ethics clearance was obtained through the Western University Research Ethics Board, University of Windsor Research Ethics Board and the University of Waterloo Office of Research Ethics. The findings from this study are intended to contribute valuable evidence to further bridge the knowledge to practice gap in patient and caregiver engagement through care transitions. Findings will be disseminated through publications, conference presentations and reports.
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 | 0.054 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".