Co-Building Hospital at Home: Qualitative Descriptive Thematic Analysis of a pan-Canadian Community Participatory Workshop Series (Preprint)
Bibliographic record
Abstract
BACKGROUND Virtual health care models, such as Hospital at Home, are an alternative to in-person care, and allow hospitals to expand care capacity and delivery without the need for additional brick-and-mortar structures. While generally well received, there is an overall lack of awareness among those receiving and giving care about what Hospital at Home is and what it does, and uncertainty about the conditions needed to implement Hospital at Home in a safe, sustainable, and equitable way. OBJECTIVE In this descriptive qualitative study, we explored public perceptions of Hospital at Home in Canada to: (1) raise awareness about the program; (2) better understand the perceived benefits and barriers to enable informed decision-making around the national hospital at home agenda; and (3) catalyse key interest holders to ensure equitable access. METHODS Healthcare providers, patients, caregivers, and hospital administrators were recruited through online social media, and professional and community networks. Five 60-minute virtual workshops were held from January to June 2025 using a “Lunch-and-Learn" format and Liberating Structures techniques. The workshops were each comprised of two parts: speaker presentations followed by participant group discussions. The workshop presentations along with 3 written observer reports (from a patient partner, an academic, and a clinician) and workshop participant Slido responses were collected and analyzed. Descriptive thematic analysis was used to identify key themes salient to the data. RESULTS Three themes were constructed from the data: (i) making Hospital at Home work for healthcare systems, (ii) making Hospital at Home work for its people, (iii) making Hospital at Home better now, and in the future. Participants reported generally positive outcomes and high satisfaction with Hospital at Home programs in Canada. Participants highlighted the need for clear communication and collaboration across care teams, technology support for staff, managing health provider and caregiver workloads, and ensuring access for rural and remote communities. There is a need to better understand the economic sustainability of the Hospital at Home program, and to study and share outcomes from Hospital at Home models to help others and refine the model. CONCLUSIONS This study provides insights into how the Hospital at Home virtual care model is perceived by providers, patients, and caregivers in the Canadian healthcare context. Our findings highlight the importance of equity, communication, care workloads, and fiscal sustainability in supporting Hospital at Home models.
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".