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Record W4234701467 · doi:10.32920/ryerson.14656479

When being imperfect Just won’t do: exploring the relationship between perfectionism and suicidality

2021· preprint· en· W4234701467 on OpenAlexaff
Richard J. Zeifman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsToronto Metropolitan UniversitySystems, Applications & Products in Data Processing (Canada)York University
Fundersnot available
KeywordsPerfectionism (psychology)ShamePsychologyClinical psychologyDepression (economics)Depressive symptomsPsychiatrySocial psychologyAnxiety

Abstract

fetched live from OpenAlex

The current study aimed to increase understanding of the relationship between perfectionism and various forms of suicidality, as well as explore potential pathways that account for the relationship. 130 university students completed measures of perfectionism, shame, difficulties with emotion regulation, self-compassion, depression severity, and hopelessness, as well explicit and implicit measures of suicidality. Results indicated that adaptive and maladaptive perfectionism were not uniquely associated with implicit and explicit suicidality. However, when not controlling for depression severity and hopelessness, higher levels of maladaptive perfectionism were associated with heightened explicit suicidality. Furthermore, results indicated that shame significantly mediated the relationship between maladaptive perfectionism and explicit suicidality. Implications for understanding the link between perfectionism and suicidality are discussed, as are potential clinical implications for reducing suicidality amongst perfectionistic individuals.

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)0.006
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.157
GPT teacher head0.358
Teacher spread0.201 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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