Skin-lightening products (SLPs) and levels of depression, anxiety, and stress among Filipino emerging adults: a cross-sectional study
Bibliographic record
Abstract
Background: The Philippines is one of Southeast Asia’s top consumers of beauty products. There have been few studies on the psychological aspects of skin-lightening products (SLPs) consumption in the Philippines. Hence, we investigated the association between knowledge of, perception towards, and frequency of use of SLP and psychological distress (depression, anxiety, and stress) levels among Filipino emerging adults. Methods: Using convenience sampling, a cross-sectional online survey collected data from Filipino emerging adults (18 to 29 years old) residing in the Philippines. Associations between knowledge, perception, and use of SLPs and psychological distress levels were estimated using generalized linear models with Poisson log-link function, adjusted for confounding factors. Effect estimates were expressed as adjusted prevalence ratio (aPR) with a 95% confidence interval (95% CI). Results: We recruited 3,127 participants (67% female; Mage =20.91, SD =2.97). High perceived benefit of SLP use is associated with increased depression levels (aPR: 1.21; 95% CI: 1.07–1.37). In addition, a high frequency of SLP use is related to decreased depression levels (20–24%) and increased anxiety levels (11–18%). Lastly, once-a-week use of SLP is linked with reduced stress levels among the participants by 35% (aPR: 0.65; 95% CI: 0.49–0.86). Conclusions: Perceptions and frequency of SLP use are suggested to be associated with psychological distress levels among Filipino emerging adults.
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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 | 0.001 | 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 | 0.001 |
| 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".