More than meets the eye. Feminist poststructuralism as a lens towards understanding obesity
Bibliographic record
Abstract
AIM: This paper presents a discussion of the application of a feminist poststructuralist-based theoretical framework as an innovative approach towards understanding and managing the complex health issue of obesity. BACKGROUND: Obesity is often viewed as a lifestyle choice for which the individual is blamed. This individualistic, dichotomous and behavioural perspective only allows for a narrow understanding of obesity and may even lead to misperceptions, stereotypes and marginalization of clients experiencing obesity. Feminist poststructuralism can provide a critical lens to understand the social construction of obesity and the broader environmental and cultural contexts of this health issue. DATA SOURCES: The theoretical framework draws from the writings of Foucault, Scott, Butler, Cheek, and Powers, published between 1983 and 2005. DISCUSSION: The concepts of discourse analysis and power relations are explored and discussed in a clear manner so that nurses can easily apply this framework to their practice as they observe, question, analyse, critique and assess the care experienced by clients who are obese. The concepts of personal and social beliefs, values and stereotypes are also discussed and examples of how to apply them in practice are provided. IMPLICATIONS: It is imperative that we continue to question our everyday nursing practices as we work to support clients, especially those who feel marginalized. This focus on power relations and reflective practice can give direction to new possibilities for change in obesity management.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.046 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".