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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,024 | 0,010 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».