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Record W2775943743 · doi:10.1177/1069072717748666

Character Strengths in Counselors: Relations With Meaningful Work and Burnout

2017· article· en· W2775943743 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Career Assessment · 2017
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsZestPsychologyBurnoutSocial psychologyPerspective (graphical)Character (mathematics)HonestyClinical psychology

Abstract

fetched live from OpenAlex

The primary goal of this study was to examine the relations from counselors’ character strengths to burnout via the potential mediating effect of meaningful work. We also compared mean levels of counselors’ character strengths to population means and conducted regression analyses to examine which character strengths uniquely predicted meaningful work and burnout. Counselors in our sample reported significantly higher levels on 13 of the 24 character strengths compared to a normed sample, with strengths like love of learning, perspective, and social intelligence being particularly elevated. Additionally, regression analyses revealed that prudence and hope predicted both meaningful work and burnout; love, perspective, and zest predicted meaningful work; and forgiveness, honesty, and self-regulation predicted burnout. These character strengths were included in the final structural equation model. Partially supporting hypotheses, prudence, perspective, and zest were related to meaningful work, which were, in turn, negatively related to burnout.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.031
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.396
Teacher spread0.372 · 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