Culture matters: Chinese mental health professionals’ fear of losing face in routine outcome monitoring
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".