Transforming primary healthcare by including the stakeholders involved in delivering care to people living in poverty: EQUIhealThY study protocol
Bibliographic record
Abstract
BACKGROUND: Ensuring access to timely and appropriate primary healthcare for people living in poverty is an issue facing all countries, even those with universal healthcare systems. The transformation of healthcare practices and organization could be improved by involving key stakeholders from the community and the healthcare system in the development of research interventions. The aim of this project is to stimulate changes in healthcare organizations and practices by encouraging collaboration between care teams and people living in poverty. Our objectives are twofold: 1) to identify actions required to promote the adoption of professional practices oriented toward social competence in primary care teams; and 2) to examine factors that would encourage the inclusion of people living in poverty in the process of developing social competence in healthcare organizations. METHODS/DESIGN: This study will use a participatory action research design applied in healthcare organizations. Participatory research is an increasingly recognized approach that is helpful for involving the people for whom the research results are intended. Our research team consists of 19 non-academic researchers, 11 academic researchers and six partners. A steering committee composed of academic researchers and stakeholders will have a decision-making role at each step, including knowledge dissemination and recommendations for new interventions. In this project we will adopt a multiphase approach and will use a variety of methods, including photovoice, group discussions and interviews. DISCUSSION: The proposed study will be one of only a few using participatory research in primary care to foster changes aimed at enhancing quality and access to care for people living in poverty. To our knowledge this will be the first study to use photovoice in healthcare organizations to promote new interventions. Our project includes partners who are targeted for practice changes and improvements in delivering primary care to persons living in poverty. By involving knowledge users, including service recipients, our study is more likely to produce a transformation of professional practices and encourage healthcare organizations to take into account the needs of persons living in poverty.
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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 | 0.067 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.074 | 0.018 |
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".