What is in a space? Exploring experiences providing or receiving patient care in unique spaces for patients waiting to transition to their next point of care in Ontario, Canada
Bibliographic record
Abstract
Introduction: Delayed discharge is a key challenge for health systems globally. Pandemic-related capacity pressures on hospitals have increasingly led to patients being moved to unique spaces (overflow units, hotels) while they wait to transition to their next point of care. However, it is unclear how patient care is managed and coordinated in these spaces or how patients and caregivers experience care in these environments. Rationale/Objective: Our study aimed to understand how to optimize care experiences and outcomes for patients with a delayed discharge, their families and care providers. The purpose of our study was to learn about people’s experiences in unique spaces, including what works well and what needs to improve. Methods: Using a qualitative descriptive design, we conducted in-depth, semi-structured interviews with patients/caregivers (n=9) and care providers (n=20; e.g., nurses, rehabilitation therapists, physicians, discharge planners) who had experience with receiving or providing care in a unique space. We interviewed participants from three different unique spaces associated with a hospital across rural and urban health regions in Ontario, Canada: a hotel previously used for patient and caregiver accommodations while receiving care away from home (beside hospital), a structured, heated tent (hospital parking lot) and a clinical building (1 hour away from hospital). Interviews were transcribed and a codebook was developed and applied to all transcripts. Thematic analysis was used to analyze the transcripts, specifically focusing on key challenges and opportunities. Results: Patient, caregiver and care provider experiences in these unique spaces included positive aspects, such as care teams focused on facilitating integrated care transitions, the opportunity to develop a collaborative team culture from the ground up and having increased interdisciplinary patient assessments. Areas of improvement were also described across interviews, such as the need for adequate space and infrastructure for optimal patient care and safety, more integration of information sharing about patient care and journeys between and across providers, patients and caregivers, more resources and support from the associated hospital and clear patient eligibility criteria for care provider referrals. Lessons learned: Unique spaces have the potential to be alternate care settings when hospitals are managing capacity pressures now and in the future; however, hospitals considering moving patients with delayed discharge to these spaces should consider both the opportunities and benefits of providing care within these environments compared to traditional hospital units. It is also important for hospitals to understand the challenges associated with providing care in these settings and develop plans to mitigate these challenges. Next steps: These findings provide learnings to inform a co-design initiative with patients, caregivers and care providers to identify best practices and resources for providing or receiving care in unique spaces that responds to patient needs as the health system continues to look to alternate care settings to ease pressures. This work will have implications on how integrated health care services are implemented in unique spaces so that patients experience a continuum of care both while they wait and as they transition to new points of care.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".