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Premium Doctors TM’ Exploration of Facial Aesthetics in Multicultural Populations in Canada and the United States

2025· preprint· en· W4411769218 on OpenAlexaboutno aff
Reza Ghalamghash

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismAestheticsFace (sociological concept)PsychologyArtSociologySocial sciencePedagogy

Abstract

fetched live from OpenAlex

Background: The field of facial aesthetics in Canada and the United States has seen significant growth, driven by societal acceptance, technological advancements, and a desire for self-enhancement. The region’s diverse demographic, with projections indicating over 50% non-Caucasian populations in the U.S. and 33% people of color in Canada by 2036, necessitates tailored aesthetic practices that respect ethnic variations in anatomy and beauty ideals. Historically, aesthetic procedures focused on Caucasian patients, often applying Westernized standards that may yield unnatural results in diverse populations. This review synthesizes evidence on facial aesthetic practices, emphasizing cultural competence, patient expectations, satisfaction, psychological impacts, and ethical considerations in multicultural North America.Methods: A systematic search was conducted across PubMed, Embase, Scopus, Web of Science, and Cochrane Library for peer-reviewed articles published primarily from 2015 to 2025. Keywords included "facial aesthetics," "multicultural populations," "ethnic beauty ideals," "surgical aesthetics," "non-surgical aesthetics," "patient satisfaction," "cultural competence," "Canada," and "United States." Inclusion criteria prioritized studies on diverse patient groups in these regions, clinical outcomes, and ethical practices. Data were extracted on anatomical variations, treatment techniques, efficacy, safety, patient-reported outcomes, and psychological impacts, then synthesized to identify trends and gaps.Results: Findings highlight significant ethnic variations in facial anatomy (e.g., skin characteristics, nasal morphology, periorbital features) and aesthetic preferences, necessitating customized surgical (e.g., rhinoplasty, blepharoplasty) and non-surgical (e.g., dermal fillers, botulinum toxin) interventions. High satisfaction is reported when cultural identity is preserved, though risks like post-inflammatory hyperpigmentation in skin of color require specialized techniques. Psychological benefits include improved self-esteem, but Body Dysmorphic Disorder (BDD) prevalence (3–53%) poses ethical challenges. Cultural competence is critical to align treatments with diverse beauty ideals and manage expectations influenced by social media.Conclusions: Successful facial aesthetic practice in multicultural North America requires a deep understanding of ethnic anatomical differences, culturally sensitive techniques, and robust psychological screening to address BDD and unrealistic expectations. Research gaps, particularly for Black, Latinx, and Indigenous populations, underscore the need for inclusive studies to ensure equitable, evidence-based care. Continuous adaptation and ethical vigilance are essential for practitioners to deliver harmonious, satisfying outcomes that respect patients’ cultural identities.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.336
Threshold uncertainty score0.676

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.012
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.149
GPT teacher head0.380
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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