Developing patient-reported experience measures to evaluate an Emergency Department Outreach Programme for people experiencing homelessness in Toronto, Canada: a community-based participatory research study protocol
Bibliographic record
Abstract
INTRODUCTION: People experiencing homelessness (PEH) have diverse and complex health and social needs and experience significant barriers to accessing support. As a result, PEH often use the emergency department (ED) for their unmet health and social needs. To address these challenges, the ED Outreach Programme at St. Michael's Hospital was developed with community partners within the Downtown East Toronto Ontario Health Team in Toronto, Canada. PEH presenting in the ED are connected to an outreach worker to identify immediate needs for a safe discharge and to access health and social supports in the community. Following discharge, the outreach worker continues to maintain contact with clients to ensure continuity of care and assists with the coordination, navigation and follow-up of healthcare and community services. METHODS: A three-phase community-based participatory research (CBPR) study will be conducted to evaluate patient experience in the ED Outreach Programme. Phase 1 involves conducting interviews with clients and staff/providers to understand their experiences with the programme and identify patient experience domains, constructs and measures. In Phase 2, programme clients, staff/providers and partners with lived experience co-design a set of patient-reported experience measures (PREMs) through focus groups. In Phase 3, the PREMs are pilot-tested to ensure their applicability and utility in the clinical setting. Central to CBPR principles, shared leadership and ownership of the project are facilitated by engaging and empowering community members. This study will contribute to new knowledge of building partnerships with PEH and marginalised communities in healthcare research, evaluation and quality improvement, particularly in developing PREMs. ETHICS AND DISSEMINATION: This study has been approved by the Unity Health Toronto Research Ethics Board (REB#21-315). Findings will be disseminated in peer-reviewed journals, conferences and forums, as well as through collaborating with partners with lived experience, community agencies and government bodies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".