Series: Public engagement with research. Part 2: GPs and primary care researchers working inclusively with minoritised communities in health research to help address inequalities
Bibliographic record
Abstract
Public engagement in health research is vital for addressing health disparities and promoting inclusivity among minoritised communities who often face barriers to accessing healthcare. Minoritised communities are groups, which have been made minorities by a dominant culture, race, ethnic group and/or social class and may experience health inequalities as a result. By incorporating diverse perspectives and lived experiences of minoritised communities, this approach aims to achieve contextually relevant research outcomes that reduce health inequalities and improve overall well-being. However, underrepresentation and lack of inclusivity challenges persist, necessitating the establishment of inclusive partnerships and grassroots participatory methodologies. To foster inclusive public engagement, it is important to overcome structural and cultural barriers, address socioeconomic challenges, and build trust with minoritised communities. This can be achieved by promoting a cultural shift that values inclusivity, providing comprehensive training to researchers, and collecting rigorous data on engagement demographics for transparency and accountability. Involving minoritised communities in decision-making through participatory research approaches enhances trust and yields successful outcomes. Additionally, allocating sufficient resources, collaborating in co-production, and prioritising the diverse needs and perspectives of stakeholders contribute to fostering inclusive public engagement in research. Overall, inclusive engagement practices particularly in primary care research have the potential to reduce health inequalities and cater to the unique requirements of minoritised communities, thereby creating more impactful outcomes and promoting equitable healthcare access.
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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.126 | 0.053 |
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".