"I think we did the best that we could in the space:” A qualitative study exploring individuals’ experiences with three unconventional environments for patients with a delayed hospital discharge
Bibliographic record
Abstract
BACKGROUND: Given growing hospital capacity pressures, persistent delayed discharges, and ongoing efforts to improve patient flow, the use of unconventional environments (newly created or repurposed areas for patient care) is becoming increasingly common. Despite this, little is known about individuals' experiences in providing or receiving care in these environments. OBJECTIVES: The objectives of this study were to: (1) describe the characteristics of three unconventional environments used to care for patients experiencing a delayed discharge, and (2) explore individuals' experiences with the three unconventional environments. METHODS: This was a multi-method qualitative study of three unconventional environments in Ontario, Canada. Data were collected through semi-structured interviews and observations. Participants included patients, caregivers, healthcare providers, and clinical managers who had experience with delayed discharges. In-person observations of two environments were conducted. Interviews were transcribed and notes from the observations were recorded. Data were coded and analyzed thematically. RESULTS: Twenty-nine individuals participated. Three themes were identified for unconventional environments: (1) implications on the physical safety of patients; (2) implications on staffing models and continuity of care; and, (3) implications on team interactions and patient care. Participants discussed how the physical set-up of some unconventional spaces was not conducive to patient needs, especially those with cognitive impairment. Limited space made it difficult to maintain privacy and develop social relationships. However, the close proximity of team members allowed for more focused collaborations regarding patient care and contributed to staff fulfilment. A smaller, consistent care team and access to onsite physicians seemed to foster improved continuity of care. CONCLUSIONS: There is potential to learn from multi-stakeholder perspectives in unconventional environments to improve experiences and optimize patient care. Key considerations include keeping hallways and patient rooms clear, having communal spaces for activities and socialization, co-locating team members to improve interactions and access to resources, and ensuring a consistent care team.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".