Health literacy – engaging the community in the co-creation of meaningful health navigation services: a study protocol
Bibliographic record
Abstract
BACKGROUND: A large proportion of patients encounter barriers to access and navigation in complex healthcare systems. They are unable to obtain information and services and to take appropriate action to improve their health. Low health literacy affects the ability of individuals to benefit from health services. Some social groups are disproportionately affected by low health literacy, including those with low educational attainment, Aboriginal people, and those on social assistance. These individuals face significant barriers in self-management of chronic diseases and in navigating the healthcare system. For these people, living in a context of deprivation contributes to maintaining disparities in access to healthcare and services. The objective of this study is to support knowledge co-construction and knowledge translation in primary care and services by involving underserved and Aboriginal people in research. METHODS: This study will integrate participatory health processes and action research to co-create, with patients, individuals, and community members impacted by health literacy, practical recommendations or solutions for facilitating navigation of the healthcare system by patients, individuals, and community members with less than optimal health literacy on how to best access health services. With this approach, academics and those for whom the research is intended will collaborate closely in all stages of the research to identify findings of immediate benefit to those impacted by health literacy and work together on knowledge translation. This study, carried out by researchers, community organizations and groups of people with low health literacy from three different regions of Quebec and Saskatchewan who can play an expert role in improving health services, will be conducted in three phases: 1) data collection; 2) data analysis and interpretation; and, 3) knowledge translation. DISCUSSION: Persons with low health literacy experience major obstacles in navigating the health system. This project will therefore contribute to addressing the gap between healthcare challenges and the needs of underserved patients with multi-morbidity and/or low health literacy who have complex health-related needs. It will pave the way for co-creating successful solutions for and with these communities that will increase their access to health services.
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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 | 0.063 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.067 | 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".