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Record W4385409280 · doi:10.1080/10503307.2023.2240949

Culture matters: Chinese mental health professionals’ fear of losing face in routine outcome monitoring

2023· article· en· W4385409280 on OpenAlexaff
Zhuang She, Hui Xu, Gina Cormier, Martin Drapeau, Barry L. Duncan

Bibliographic record

VenuePsychotherapy Research · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsGratitudePsychologyModerationPerspective (graphical)Coping (psychology)Mental healthFace-to-faceSelf-efficacyFace (sociological concept)Clinical psychologySocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

OBJECTIVE: The culturally salient fear of losing face might influence Chinese therapists' attitudes toward and use of routine outcome monitoring (ROM). We tested a model wherein self-face concern is associated with ROM use by way of attitudes toward ROM, and whether this process is weakened when therapists report high counseling self-efficacy and perspective-taking. METHOD: = 371) completed questionnaires on their fear of losing face, attitudes toward ROM, ROM use, counseling self-efficacy, and perspective-taking. RESULTS: Regression-based analyses showed that fear of losing face was linked to greater negative attitudes toward ROM and lower ROM use. Greater negative attitudes mediated the relationship between fear of losing face and ROM use. However, neither counseling self-efficacy nor perspective-taking mitigated the relationship between self-face concern and ROM use; instead, they exacerbated this relationship through different paths. In the mediated pathway, counseling self-efficacy in coping with clients with difficult problems interacted with self-face concern to predict negative attitudes toward ROM. Perspective-taking served as a moderator that exacerbated the direct relationship between self-face concern and ROM use. CONCLUSIONS: Findings suggest the importance of considering culturally salient factors in implementing ROM in China and other non-Western contexts.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.151
GPT teacher head0.566
Teacher spread0.416 · 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.

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

Citations10
Published2023
Admission routes1
Has abstractyes

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