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Record W2033607009 · doi:10.1080/00224540109600548

The Relation Between Well-Being, Impostor Feelings, and Gender Role Orientation Among Canadian University Students

2001· article· en· W2033607009 on OpenAlexaffabout
Aysa N. September, Michael McCarrey, Anna Baranowsky, Chantal Parent, Dwayne Schindler

Bibliographic record

VenueThe Journal of Social Psychology · 2001
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFeelingPsychologyAutonomyPersonal developmentPurpose in lifePsychological well-beingScale (ratio)Social psychologyDevelopmental psychologyClinical psychologyPsychotherapist

Abstract

fetched live from OpenAlex

A theoretical model of well-being identifies 6 key components that have been examined primarily in older adults (e.g., C. D. Ryff, 1989c, 1991): self-acceptance, positive relations with others, autonomy, environmental mastery, purpose in life, and personal growth (C. D. Ryff, 1995; C. D. Ryff & C. L. M. Keyes, 1995; C. D. Ryff & B. Singer, 1996). The authors examined them in a sample of 379 Canadian university students to determine how well-being was correlated with endorsement of stereotypic gender roles and with the impostor phenomenon. The participants completed Ryff's Scales of Psychological Well-Being (Ryff, personal communication, March 1996), the Clance Impostor Phenomenon Scale (P. R. Clance & M. A. O'Toole, 1988), and the Extended Personal Attributes Questionnaire (J. T. Spence, R. L. Helmreich, & C. K. Holahan, 1979). The results supported the hypotheses that (a) people with higher scores for expressive traits score higher for well-being stemming from positive relations with others, (b) people with higher scores for instrumental traits score higher for well-being related to feelings of autonomy, (c) people with higher scores for impostor feelings (and lower scores for ability confidence) score lower for self-acceptance and (d) for environmental mastery.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.329
Teacher spread0.310 · 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 teacher head, not a consensus.

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

Citations86
Published2001
Admission routes2
Has abstractyes

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