Exploring Dietitians’ Engagement with Health Literacy: Concept and Practice
Bibliographic record
Abstract
PURPOSE: Recognition of health literacy as a serious problem in Canada calls for all health practitioners to rethink how they provide health information. This qualitative research study explored how Canadian dietitians understand the multifaceted concept of health literacy and, if and how, they apply it in their practice. METHODS: Nine dietetic or nutrition practitioners from different practice settings were purposely selected through an environmental scan of health literacy interventions, professional networks, and interviewee snowballing. Qualitative data were collected using conversational-style personal interviews and thematically analyzed through an iterative process of constant comparison. RESULTS: All participants recognized value in addressing health literacy in their practice with many barriers and enablers to its application identified. Participants referred to difficulties in communicating nutrition information to people with low levels of functional literacy, reflective of a deficit approach to health literacy. However, practices consistent with the more empowering concepts of interactive and critical health literacy, reflective of an asset-based approach, were also described. CONCLUSION: This research provides a preliminary picture of how dietitians engage with health literacy in various settings in Canada and suggests implications for developing strengths-based health literacy approaches to dietetic practice.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".