Reliability and validity of the Short Inventory of Mindfulness CapabilityChinese version applied in multiethnic college students
Bibliographic record
Abstract
Abstract Objective:To evaluate the reliability and validity of the Short Inventory of Mindfulness Capability Chinese version(SIMC)applied in multiethnic college students,in order to provide measurement tools for studies related to mindfulness.Methods:A total of 990 college students in the Ningxia Hui Autonomous Region were selected,495 students were selected by random number table for exploratory factor analysis,and the other 495 students for confirmatory factor analysis.The criterion validity was tested by the Depression Anxiety Stress Scale(DASS21)and the Toronto Alexithymia Scale(TAS20).At a fourweek interval,137 students were randomly selected from the overall sample for retest,and the retest reliability was tested by the intergroup correlation coefficient(ICC).Results:Item analysis showed that 12 items of SIMC had good degree of differentiation.Exploratory factor analysis extracted three factors of acting with awareness,describe and nojudge,and the cumulative contribution rate was 56.63%.The confirmatory factor analysis showed that the scale structure fitted well.The total score of SIMC was negatively correlated with the depression,anxiety,stress of DASS21 and the inability to recognize emotions,the inability to describe emotions and the extraversion thinking of TAS20(P<0.01).Multiple regression analysis showed that acting with awareness and describe were factors that affected depression,anxiety and stress.Cronbach′s α coefficient of the total score and each dimension was 0.614~0.798.The ICC was 0.571~0.636 (P<0.01).Conclusions:SIMC had good validity and reliability in the multiethnic college students,and could be used as a measurement tool for research on mindfulness.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".