Guidance for 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 (HaH), 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 HaH is and what it does, and uncertainty about the conditions needed to implement HaH in a safe, sustainable, and equitable way. Objective: In this descriptive qualitative study, we aimed to synthesize key interest holder-generated perspectives about the implementation, sustainability, and equity of HaH models in Canada using data generated through a national participatory workshop series. Methods: Health care providers, patients, caregivers, and hospital administrators were recruited to participate in the workshop series through social media, 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 each comprised 2 parts: speaker presentations followed by participant group discussions. Workshop presentations, along with 3 written observer reports (from a patient partner, an academic, and a clinician), and workshop attendee polling responses, were collected and analyzed. Descriptive thematic analysis was used to construct key themes salient to the data. Results: Three themes were constructed from the data: (1) making HaH work for health care systems, (2) making HaH work for its people, and (3) making HaH better now and in the future. Participants reported generally positive outcomes and high satisfaction with HaH programs in Canada. Participants highlighted the need for clear communication and collaboration across care teams, technology support for staff, managing health care provider and caregiver workloads, and ensuring access for rural and remote communities. There is a need to better understand the economic sustainability of the HaH program and to study and share outcomes from HaH models to help others and refine the model. Conclusions: This study provides insights into how the HaH virtual care model is perceived by health care providers, patients, caregivers, and hospital administrators in Canada. Our findings highlight the importance of equity, communication, care workloads, and fiscal sustainability in supporting HaH 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 | 0.039 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.032 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".