Bibliographic record
Abstract
Cosmetic medical treatments have become mainstream, and images of beauty surround us on television, in magazines, and in advertising. It is no wonder that the quest for beauty has become so prevalent. This paper explores why individuals choose to undergo cosmetic procedures, and looks at the nature versus nurture debate surrounding this phenomenon. It is important for nurses, physicians, nurse practitioners, or other healthcare professionals involved in the cosmetic surgery field to understand the underlying motivations for choosing to undergo elective cosmetic procedures in order to make appropriate choices about their patients' care. The first theory in this article is rooted in the "nature" school-of-thought and explores the evolutionary basis behind the quest for beauty. It shows that we may be 'hardwired' to think that our appearance signals our reproductive capability (D. B. Sarwer, L. Magee, & V. Clark, 2004) and that human physical attractiveness is merely a collection of physical traits that signal fecundity and health (V. Swami, C. Greven, & A. Furnham, 2007). The "nurture" concept focuses on the second theory, the sociocultural theory, which implies that people who choose to use cosmetic medical treatments to enhance their appearance may be attempting to increase their self-image or self-perception, improve their social relationships, and increase their probability of success across a variety of social situations. Other minor theories such as the estrogen theory and the psychological theory are discussed, along with implications for practice. All of these theories are valuable to the healthcare professional and allow a deeper understanding of the psyche of their patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.282 | 0.123 |
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".