A codevelopment process to advance methods for the use of patient‐reported outcome measures and patient‐reported experience measures with people who are homeless and experience chronic illness
Bibliographic record
Abstract
INTRODUCTION: People who experience social disadvantage including homelessness suffer from numerous ill health effects when compared to the general public. Use of patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) enables collection of information from the point of view of the person receiving care. Involvement in research and health care decision-making, a process that can be facilitated by the use of PROMs and PREMs, is one way to promote equity in care. METHODS: This article reports on a codevelopment and consultation study investigating the use of PROMs and PREMs with people who experience homelessness and chronic illness. Data were analysed according to interpretative phenomenological analysis. RESULTS: Committee members with lived experience identified three themes for the role of PROMs and PREMs in health care measurement: trust and relationship-building; health and quality of life; and equity, alongside specific recommendations for the design and administration of PROMs and PREMs. The codevelopment process is reported to demonstrate the meaningful investment in time, infrastructure and relationship-building required for successful partnership between researchers and people with lived experience of homelessness. CONCLUSION: PROMs and PREMs can be meaningful measurement tools for people who experience social disadvantage, but can be alienating or reproduce inequity if they fail to capture complexity or rely on hidden assumptions of key concepts. PATIENT OR PUBLIC CONTRIBUTION: This study was conducted in active partnership between researchers and people with experience of homelessness and chronic illness, including priority setting for study design, data construction, analysis and coauthorship on this article.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".