Partnerships and participation in conducting poverty-related health research
Bibliographic record
Abstract
Partnerships and participation are important principles of primary health care and increasingly are advocated in poverty-related health research. These concepts were central to our study of low-income Canadians’ use of health-related services and supports. We created partnerships by including community members on the research team, establishing a community advisory committee, involving community agencies in recruitment and interviews, and obtaining stakeholder perspectives on the research findings. We fostered participation of low-income people by including their perspectives during the design and implementation of the study, reducing barriers for participation, and using peer interviewers. Despite the successes of these strategies for promoting partnerships and participation, challenges emerged in relation to maintaining involvement of stakeholders and ensuring adequate opportunities for research capacity building. Stakeholders and lowincome people identified policy and service delivery changes that would enhance accessibility to health-related services as well as potential barriers that influence change efforts. Participants emphasized the need to view poverty as ‘everyone’s business’, which necessitates participation and partnerships with people living in poverty, with service providers, and with policy influencers. Our findings suggest, however, that people living in poverty encounter barriers to participation, and that further efforts are needed to obtain their input in the development of programmes, services, and policies. We recommend that primary health care research include community partners on the research team, community advisory committees and perspectives of policy makers and service providers. Researchers require adequate funding to develop and maintain partnerships with stakeholders, to train and support vulnerable people in developing skills and confidence as active research participants and to communicate the research to relevant stakeholders. We also recommend that evaluation of the participatory process be built into the research design.
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 | 0.455 | 0.311 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.033 | 0.026 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.005 | 0.044 |
| Research integrity | 0.006 | 0.011 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".