Validation of the english version of the Multidimensional Mentalizing Questionnaire (MMQ)
Bibliographic record
Abstract
BACKGROUND: Mentalizing refers to the ability to understand one's own and others' mental states. Mentalizing is considered a key component of social cognition and healthy personality development. A multinational assessment tools able to appraise the multidimensional and multifaceted aspects of this complex construct are needed. OBJECTIVE: The present study had two aims: (a) validate an English version of the Multidimensional Mentalizing Questionnaire (MMQ, 33 items) which was designed to assess mentalizing based on an integrated and multilevel model of mentalizing; (b) explore the correlational relationships between the six dimensions of the MMQ and a set of sociodemographic, psycho-cognitive, mental health, and socio-functional variables. METHODS: Overall, 1823 individuals (age: 19-76 years old [M = 45; SD = 16]; sex: male = 48.51%, female = 50.57%, non-binary = 0.9%) participated in an online survey. While the participants came from 77 different countries, most of them were residents in UK and USA (95%). Data analytics include confirmatory factorial analysis and Pearson correlations. RESULTS: The CFA results validated the factorial structure of a 28-items MMQ-English version, with acceptable goodness of fit indices. Regarding the psychometric properties, the MMQ-English version showed good internal reliability and significant positive correlation with another scale designed to assess an analogue construct showing a fair convergent validity. The findings indicated that males, individuals with lower levels of education, lower socio-economic status, depressed, and with a higher score of loneliness are significantly more likely to report poor mentalizing compared with females, individuals with higher education level, greater SES, happier, and with lower scores of loneliness. CONCLUSION: The present study validated the English version of the MMQ.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".