A Critical Discourse Analysis of Risk for Preventing Type 2 Diabetes from Primary Care Provider, At-Risk Patient, and Canadian Policy Perspectives
Bibliographic record
Abstract
Global estimates suggest over 1.3 billion individuals will be living with diabetes by 2045, of which up to 96% of cases are type 2 diabetes (T2D), a disease that may be prevented or delayed. This research program explored the discourses of risk for T2D to understand how language use shapes how we come to understand, know (or not know) about risk for the disease, and the effect on how we are willing to prevent the disease. This research program consisted of three studies through the lens of: 1) primary care providers; 2) at-risk patients; and 3) Canadian policy. Foucauldian discourse analysis was applied utilizing thematic analysis to understand the discourses of risk for T2D. Study 1 included semi-structured interviews with primary care providers (n=14) working in a family health team. Findings suggest providers use the ‘prediabetes’ threshold to open up risk and preventative discourses but this can also delay communicating about risk for the disease until the threshold is reached. Study 2 included two focus groups and two interviews with patients who were told by a provider they were at a high-risk or had prediabetes. Findings suggest that patients use prediabetes to navigate their attendance to risk for the disease, but struggle with their understanding of the clinical purpose of prediabetes and how to reduce their risk for the disease. Study 3 included a discourse analysis of policy texts and the current Framework for Diabetes in Canada (n=7). Data were analyzed deductively through application of Carol Bacchi’s ‘what’s the problem represented to be’ framework. Findings suggest the problem of risk for T2D is represented as Canadians not being aware of risk factors for the disease and needing to be screened, identified, and educated about the risks for the disease. This was problematized as an individual’s problem to deal with their risk of T2D, rather than attending to the social and environmental conditions which create and maintain the risk. With rates of T2D expected to continue to increase in the future, there is a need to consider how language governs what we are willing to do to address risk for T2D.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".