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The Coping Inventory for Stressful Situations: Factorial Structure and Associations With Personality Traits and Psychological Health1

2000· article· en· W2023287183 on OpenAlexaff
Richard Cosway, Norman S. Endler, Andrew Sadler, Ian J. Deary

Bibliographic record

VenueJournal of Applied Biobehavioral Research · 2000
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyCoping (psychology)PersonalityClinical psychologyBig Five personality traitsPersonality Assessment InventoryTransactional leadershipDistressPsychological distressMental healthSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Principal components analyses on the Coping Inventory for Stressful Situations (C1SS; Endler & Parker, 1990a) were carried out for 730 Scottish doctors and farmers. Endler and Parker's three‐factor structure was supported both in the male and female subgroups and in the two occupational groups. Intercorrelations of the C1SS factors with personality factors of the NEO‐Five Factor Inventory and a self‐reported psychological distress scale, the General Health Questionaire‐28, provided predictive validity for the C1SS in the transactional model of stress.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.272
GPT teacher head0.552
Teacher spread0.280 · 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

Citations180
Published2000
Admission routes1
Has abstractyes

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