Health Literacy and Patient Experience
Bibliographic record
Abstract
Indigenous Peoples continue to experience considerably lower health outcomes than non-indigenous Canadians. In Canada low health literacy is a cause for concern with 60% of adult Canadians lacking the knowledge and skills to manage their health and health-care needs. Currently, there is limited understanding of Indigenous peoplesu2019 health literacy needs. In this presentation, we describe a new program that aims to improve health outcomes for Indigenous women and children with complex conditions and tool for measuring health literacy based on Indigenous approaches to health.Health Literacy is defined as capacity of individuals to gain access to, understand and use information in ways which promote and maintain good health. It is shaped by both individual and systemic factors. Low Health literacy is associated with increased mortality, poor adherence to prescribed medications, difficulty communicating with health professionals, and poorer knowledge about disease processes and self-management skills among people with chronic conditions. Thus, health literacy measurement can provide pertinent information to practitioners and healthcare organizations about interventions needed to improve health outcomes.The Indigenous Complex Care team (ICCT) provides an innovative service under BC Womenu2019s and Childrenu2019s Hospitals Indigenous Health Program. Launched in 2017, its aim is to improve discharge planning, coordination of care and health outcomes for Indigenous women and children with complex care needs. Improved health literacy among ICCT patients/families is one of the key intended outcomes for the program and a key determinant of womenu2019s ability to successfully manage their health. Yet, Indigenous-based tools to assess health literacy are currently lacking.Osborne et al (2013) developed a Health Literacy Questionnaire (HLQ), which comprises nine independent constructs of health literacy. Although this questionnaire is one of the most comprehensive health literacy tools in current literature, it was not developed for the Indigenous population. Namely, it does not incorporate Indigenous understandings of health and wellness. Out research addresses this using this tool. We are in the process of validating the tool. It shows potential for measuring and promoting health literacy of Indigenous women in other contexts.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".