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Record W2911826687 · doi:10.17744/mehc.41.1.05

Transcendent Characteristics as Predictors of Counselor Professional Quality of Life

2019· article· en· W2911826687 on OpenAlexaff
Brandon R. Browning, Ryon C. McDermott, Marjorie E. Scaffa

Bibliographic record

VenueJournal of Mental Health Counseling · 2019
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsBrandon University
Fundersnot available
KeywordsGratitudeBurnoutPsychologyCompassionClinical psychologyLife satisfactionQuality of life (healthcare)Compassion fatigueQuality (philosophy)Social psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Although counselors do meaningful work, they can also experience compassion fatigue as a result of counseling clients. Relatively few studies have focused on how counselors’ positive characteristics might contribute to their professional quality of life. This study combined the transcendent constructs of gratitude, hope, and daily spiritual experiences as predictors of counselor burnout and compassion satisfaction. Counselors (N = 98) completed a survey assessing professional quality of life and constructs of gratitude, hope, and daily spiritual experiences. After controlling for demographic variables, simultaneous regression analysis revealed that gratitude and daily spiritual experiences were significant negative predictors for burnout. Older counselors were also less likely to have burnout, highlighting questions about what strengths are developed with age. Hope was the only significant predictor of compassion satisfaction. Results suggest that these predictors are differentially related to aspects of professional quality of life and may hold promise as potential protective factors.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0050.000

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 head0.037
GPT teacher head0.396
Teacher spread0.359 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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