Reacting to news of being at-risk for type-2 diabetes: An exploratory study of risk reactions
Bibliographic record
Abstract
Prediabetes afflicts more than five million Canadians (Public Health Agency of Canada, 2014) and is characterized by impaired glucose regulation that increases the risk of developing type 2 diabetes (T2D). People with chronic conditions, including T2D, often experience negative reactions about their condition. These experiences can compromise self-regulatory efforts to manage illness through lifestyle behaviours. Although research surrounding people's experiences of living with T2D has been conducted, less is known about how individuals respond when they learn they are at-risk for T2D. Receptivity of prediabetes status is of relevance to managing lifestyle behaviours, as behavioural modification at this stage can prevent disease progression to T2D. The purpose of this study was to explore how people who learn that they are at-risk for T2D (prediabetic) process, react to and experience this information. Seven adults (Mage= 58.57, SD = 2.64; 6 female, 1 male) engaged in an in-depth, semi-structured interview (M = 56 min). Interpretative phenomenological analysis (Smith & Osborn, 2007) was used to analyze the data using an inductive approach. Five themes emerged from the data related to the study purpose: (a) distress and concern, (b) downplay and lack of knowledge of T2D risks, (c) attributions to and guilt for past behaviour, (d) self-criticism, and (e) common humanity. Findings suggest that people experience negative reactions related to their T2D risk but also exhibit self-compassionate responses. These findings can inform lifestyle behaviour change programs for individuals living with pre-diabetes by providing a better understanding of the patient's perspectives of disease diagnosis.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".