Codesigning person‐centred quality indicators with diverse communities: A qualitative patient engagement study
Bibliographic record
Abstract
INTRODUCTION: Effective engagement of underrepresented communities in health research and policy remains a challenge due to barriers that hinder participation. Our study had two objectives: (1) identify themes of person-centred care (PCC) from perspectives of diverse patients/caregivers that would inform the development of person-centred quality indicators (PC-QIs) for evaluating the quality of PCC and initiatives to improve PCC and (2) explore innovative participatory approaches to engage ethnocultural communities in qualitative research. METHODS: Drawing on participatory action research methods, we partnered with a community-based organization to train six 'Community Brokers' from the Chinese, Filipino, South Asian, Latino-Hispanic, East African and Syrian communities, who were engaged throughout the study. We also partnered with the provincial health organization to engage their Patient and Family Advisory, who represented further aspects of diversity. We conducted focus group discussions with patients/caregivers to obtain their perspectives on their values, preferences and needs regarding PCC. We identified themes through our study and engaged provincial stakeholders to prioritize these themes for informing the development of PC-QIs and codesign initiatives for improving PCC. RESULTS: Eight focus groups were conducted with 66 diverse participants. Ethnocultural communities highlighted themes related to access and cost of care, language barriers and culture, while the Patient and Family Advisory emphasized patient and caregiver engagement. Together with provincial stakeholders, initiatives were identified to improve PCC, such as codesigning innovative models of training and evaluation of healthcare providers. CONCLUSION: Incorporating patient and community voices requires addressing issues related to equity and understanding barriers to effective and meaningful engagement. PATIENT OR PUBLIC CONTRIBUTION: Patient and public engagement was central to our research study. This included partnership with a community-based organization, with a broad network of ethnocultural communities, as well as the provincial health service delivery organization, who both facilitated the ongoing engagement of diverse patients/caregiver communities throughout our study including designing the study, recruiting participants, collecting and organizing data, interpreting findings and mobilizing knowledge. Drawing from participatory action research methods, patients and the public were involved in the codesign of the PC-QIs and initiatives to improve PCC in the province based on the findings from our study.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".