Effect of body image perception and skin-lightening practices on mental health of Filipino emerging adults: a mixed-methods approach protocol
Bibliographic record
Abstract
INTRODUCTION: The rampant distribution of idealised images on the internet may lead the general public to improve their body appearance in a way that is sometimes excessive, compulsive or detrimental to other aspects of their lives. There is a decreasing appreciation of body image among emerging adults and an increasing trend on skin-lightening practices linked with psychological distress. This protocol describes the mixed-method approach to assess the relationships among body image perception, skin-lightening practices and mental well-being of Filipino emerging adults and determine the factors that influence them. METHODS AND ANALYSIS: An explanatory sequential mixed-method approach will be used. A cross-sectional study design will involve an online self-administered questionnaire of 1258 participants, while a case study design will involve in-depth interviews with 25 participants. Data analysis will use generalised linear models and structural equation modelling with a Bayesian network for the quantitative data. Moreover, the qualitative data will use an inductive approach in thematic analysis. A contiguous narrative approach will integrate the quantitative and qualitative data. ETHICS AND DISSEMINATION: The University of the Philippines Manila Review Ethics Board has approved this protocol (UPMREB 2022-0407-01). The study results will be disseminated through peer-reviewed articles and conference presentations.
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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 | 0.033 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.005 |
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".