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Record W2805917731 · doi:10.29173/spectrum13

The Brunner-Lei-Peters Resilience Scale

2018· article· en· W2805917731 on OpenAlexaffvenue
Lexi Jace Brunner

Bibliographic record

VenueSpectrum · 2018
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyScale (ratio)Coping (psychology)Clinical psychologyPsychological resilienceMental healthConstruct validityPopulationContext (archaeology)Reliability (semiconductor)Resilience (materials science)PsychometricsPsychiatrySocial psychologyMedicineCartographyEnvironmental healthGeography

Abstract

fetched live from OpenAlex

Resilience is an important attribute for mental health. Positive benefits felt by resilient individualsinclude fewer depressive episodes, and better coping strategies. Due to the positive outcomesexperienced by resilient people, it may be clinically useful to examine this construct within apsychotherapeutic context. Accordingly, we created the Brunner-Lei-Peters Resilience Scale, whichincluded a preliminary test of 70 items and 10 validity items; we tested the scale on a sample of 150people. Participants were representative of the general population with about an equal representationof gender and a wide age range from 18 to 66 years of age. Although the scale initially included 70items, through psychometric analysis, we reduced the scale to 15 items, while maintaining soundpsychometric validity and reliability. Clinical implications and limitations of the scale are discussed.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.012
GPT teacher head0.358
Teacher spread0.345 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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 routes2
Has abstractyes

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