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Record W2207245451 · doi:10.1177/0829573515594372

When Ideals Get in the Way of Self-Care

2015· article· en· W2207245451 on OpenAlexaff
Richard J. Zeifman, Sarah K. Atkey, Rebecca Young, Gordon L. Flett, Paul L. Hewitt, Joel O. Goldberg

Bibliographic record

VenueCanadian Journal of School Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsYork UniversityUniversity of British ColumbiaToronto Metropolitan University
Fundersnot available
KeywordsPsychologyPerfectionism (psychology)Mental illnessClinical psychologySelf-conceptIntervention (counseling)Scale (ratio)Stigma (botany)PremiseMental healthSocial psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

In the current study, we investigated whether adolescents high in perfectionism are prone to experiencing self-stigma for seeking psychological help. This work is based on the premise that the need to seek help for psychological difficulties is not consistent with idealistic personal goals of perfectionistic young people and their desire to retain an idealistic self-image. A sample of 85 high school students completed the Child and Adolescent Perfectionism Scale, the Self-Stigma of Seeking Help Scale, and a measure of contact with individuals with mental illness. Results indicated that perfectionism was associated with self-stigma among those students with little to no experience with people with a history of mental illness. These findings suggest that certain perfectionistic students have a propensity toward low self-acceptance and judge themselves negatively for needing help. Implications are discussed for prevention and intervention programs that emphasize contact and experiential opportunities with individuals who have mental illness.

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)0.003
metaresearch head score (Gemma)0.012
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.009
Scholarly communication0.0050.004
Open science0.0010.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.053
GPT teacher head0.344
Teacher spread0.291 · 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

Citations23
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of School PsychologySame topicPerfectionism, Procrastination, Anxiety StudiesFrench-language works237,207