Frequency of Use of Skin-Lightening Products, Levels of Self-Esteem and Colorism Attitudes in University Students of Karachi: A Cross Sectional Study
Bibliographic record
Abstract
The objective of our study was to determine the frequency of use of skin lightening/whitening products, level of self-esteem among university students, and any association between them. A cross-sectional study was conducted at Jinnah Medical and Dental College and the University of Karachi over a period of 12 months on a convenient sample of 499 students of both genders. Self-esteem was measured using Rosenberg Self Esteem Scale, and the use of skin whitening products and attitude towards skin color was determined using a structured questionnaire. Data were analyzed using SPSS 21.0. Out of n = 499 participants, 30.9% (n = 155) responded that they had used skin lightening products during their life. Of those who said yes, 15.1% (n = 76) responded that they are currently using such products. 63.8% (n = 321) classified their complexion as medium, 29.8% (n = 150) classified it as fair and 5.2% (n = 26) classified themselves as having dark complexion. 73% (n = 367) of people were completely satisfied with their complexion, while 20.1% (n = 101) wished for a lighter shade. Self-esteem scores were calculated, and it was found that 89.9% (n = 452) participants had scores above 15, and only 9.3% (n = 47) participants had low self-esteem, having scores below 15. Only 1% (n = 5) having dark complexion, 5.4% (n = 27) having medium complexion and 2.8% (n = 14) fair suffered from low self-esteem score. The frequency of use of skin lightening products was found to be low in university students from the sample population. The majority had good self-esteem and were satisfied with their complexion. There was no relationship between complexion and self-esteem in the study sample.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".