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Record W4243948891 · doi:10.17758/eares2.eap0618126

A Survey of Students' Cognition of Acne and Their Self-Identity: A Case Study of a College in North Taiwan

2018· article· en· W4243948891 on OpenAlexaff
Wei-Che Sung, Michel Plaisent, Prosper Bernard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicAcne and Rosacea Treatments and Effects
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsIdentity (music)CognitionComputer scienceAcnePsychologyMathematics educationMedicineDermatologyArtPsychiatryAesthetics

Abstract

fetched live from OpenAlex

Acne is one of the common skin problems in adolescents and it is a multifactorial inflammatory disease of the skin.Facial acne affects not only appearance and mood, but also causes permanent scars on the patient's skin and even social problems.This study intends to use cosmetic and application science students from a certain specialty school in the northern part of the country as subjects to investigate the impact of cosmetics students on acne cognition and self-identity.The results showed that as many as 80% of the respondents had problems with acne, especially in the forehead, nose T-site; most students think that facial acne can cause confusion, inferiority and affect social interaction, the average degree of recognition of 3.8 points ( 5 subscales).The student's average recognition rate for acne is 71.3%, and he believes that frequent staying up late, eating, stress, hormonal changes, improper facial cleansing are the causes of acne, less staying up late, looking at dermatologists, changing eating habits, and Facial cleansing is a way to improve acne.It is inferred that the students' knowledge and treatment of acne are still insufficient.They must strengthen the teaching of acne-related professional knowledge in beauty courses to prevent skin problems from affecting students' self-identity.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.322
Teacher spread0.296 · 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
Published2018
Admission routes1
Has abstractyes

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