Implications for clients when nurses view weight as main cause of Type 2 diabetes in primary care
Bibliographic record
Abstract
Type 2 diabetes (T2D) is often seen as primarily caused by weight, and its amelioration associated with individual behaviour change, which has the potential for negative consequences for people living with the disease. The aims of this study were to explore how weight was framed by diabetes resource nurses and to determine the implications of that framing for nurse practice in a primary care setting in Australia. The research was a qualitative empirical case study using semistructured interviews with nurses focusing on meanings and interpretations. The findings were interpreted using a constructivist epistemology of both inductive and deductive inference. The study found that nurses viewed overweight and obesity as unhealthy and the primary causes of T2D, and that weight was frequently discussed in the health care encounter. Nurses emphasised individual responsibility through behaviour change to manage T2D, downplaying other known causes such as age and family history and important social inequalities. Studies show that nurses have negative attitudes towards overweight and obese patients. The implications of this research are that the nurses' views could potentially negatively affect clients' management of T2D, which has the potential for poor health outcomes.
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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 | 0.021 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".