Managing “socially admitted” patients in hospital: a qualitative study of health care providers’ perceptions
Bibliographic record
Abstract
BACKGROUND: Emergency departments are a last resort for some socially vulnerable patients without an acute medical illness (colloquially known as "socially admitted" patients), resulting in their occupation of hospital beds typically designated for patients requiring acute medical care. In this study, we aimed to explore the perceptions of health care providers regarding patients admitted as "social admissions." METHODS: This qualitative study was informed by grounded theory and involved semistructured interviews at a Nova Scotia tertiary care centre. From October 2022 to July 2023, we interviewed eligible participants, including any health care clinician or administrator who worked directly with "socially admitted" patients. Virtual or in-person individual interviews were audio-recorded and transcribed, then independently and iteratively coded. We mapped themes on the 5 domains of the Quintuple Aim conceptual framework. RESULTS: = 13). We categorized 9 themes into 5 domains: patient experience (patient description, provision of care); care team well-being (moral distress, hierarchy of care); health equity (stigma and missed opportunities, prejudices); cost of care (wait-lists and scarcity of alternatives); and population health (factors leading to vulnerability, system changes). Participants described experiences caring for "socially admitted" patients, perceptions and assumptions underlying "social" presentations, system barriers to care delivery, and suggestions of potential solutions. INTERPRETATION: Health care providers viewed "socially admitted" patients as needing enhanced care but identified individual, institutional, and system challenges that impeded its realization. Examining perceptions of the people who care for "socially admitted" patients offers insights to guide clinicians and policy-makers in caring for socially vulnerable patients.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".