The Hurt of Judgment in Excessive Weight Women: A Hermeneutic Study
Bibliographic record
Abstract
Excess weight is one of the increasing problems of the present society and one of the threatening health conditions around the world. Despite many efforts for prevention and treatment or even surgery, the process of excess weight is not decreased in the world. While most of the studies conducted on excess weight concentrated on the issues why people get excess weight or how the prevention and treatment of excess weight must be performed, there is lake of knowledge about what excessive weight people really experience in their daily life. Understanding the lived experience of excess weight in women is linked with their health and society's health while it indirectly develops the nursing knowledge to improve the quality and access to holistic health care in excessive weight women. The aim of study was to describe with a deeper understanding, the lived experience of excess weight in women. Using a hermeneutic phenomenological approach and a van-manen analysis methods, in depth semi- structured interviews were conducted with twelve women who had lived experience of excess weight. The hurt of Judgment was the main theme that emerged in the process of data analysis. This theme was derived from three sub-themes including social judgment, being different and being seen. These findings can prove helpful in promoting the nursing knowledge concerning a holistic approach in communicating to excessive weight people.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".