Framing the Underlying Root Causes of Equitable Primary Health Care Access Challenges Faced by Racialized/Immigrant Community: A Community-Engaged Concept Mapping Research
Bibliographic record
Abstract
Background: Primary health care encompasses all the services within a community that address the daily health requirements of citizens across all life stages. Access to primary care plays a crucial role in upholding good health. However, immigrant/racialized communities, arriving from diverse cultural and socio-demographic backgrounds, often face health maintenance challenges in their new country. This situation creates disparities in accessing primary health care services, subsequently impacting their overall well-being. Aim(s): This study utilizes a community-based participatory research approach to capture racialized/immigrant communities' experiences while accessing primary health care in Canada. The broad objective is to provide a community-vetted framework to explain the barriers to inform interventions that increase healthcare accessibility. Methods: A community advisory group will be engaged with the research team in all phases of this research. We propose activities in two phases: (i) Group Concept Mapping of primary health care access barriers and (ii) Root Cause Framework Construction of the identified barriers to understanding “what leads to what”. We will perform 20 different FGD sessions with the immigrant/racialized community members to create a barriers list, sort them into piles, rate them and create final cluster maps using concept system software. Finally, we will validate the final cluster solutions with the respondents in separate sessions with them. The root causes of each cluster of barriers will be identified with our transdisciplinary research team using the “what leads to what” technique and corroborating the diagram with the community through World Café events. Finally, we will conduct one-on-one interviews with different key care stakeholders and develop a cause-and-effect diagram using the Ishikawa model. Results: The expected outcomes of this project include the development of a framework that can help inform interventions to increase healthcare accessibility of racialized/immigrant communities. Conclusion: Conducting research on this topic will illuminate the unique healthcare challenges faced by immigrant/racialized communities, enabling the development of tailored interventions that promote equitable access and improved health outcomes.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".