Engaging patients and families in developing, implementing, and evaluating hospital at home: A Canadian case study
Bibliographic record
Abstract
The Hospital at Home (HaH) care model is naturally patient-centred, with improved patient and family experiences and outcomes firmly anchoring the innovative approach to care. Existing literature focuses largely on the health care and patient care outcomes of HaH; however, to date, none of the identified literature has reported on engaging patients and families in the development, implementation, or evaluation of the HaH model of care. A multi-stakeholder, Patient-Oriented Research team in Victoria, British Columbia, Canada engaged patients and family/friend caregivers (PFCs) across all components of the HaH program. Guided by best practices in patient and public engagement, the team collaborated to 1) explore the potential impact of in-home acute care on PFCs’ experiences; 2) identify health, social, and practice outcomes that matter to PFCs; 3) examine the social and environmental factors which may impact delivery of HaH; and 4) inform the HaH evaluation framework that includes PFC priority measures related to experience and outcomes. A public, online survey (n=543 PFC respondents) revealed both program-specific and evaluation-specific themes. These included a focus on patients achieving their own health goals and standard health outcomes, as well as patients and caregivers receiving training to support care at home. Engaging PFCs throughout HaH conception and implementation ensured the end program accurately reflected the priorities, concerns, and values of those that HaH is meant to serve. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 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".