Mental States Task (MST): Development, Validation, and Correlates of a Self‐Report Measure of Mentalization
Bibliographic record
Abstract
OBJECTIVES: Mental states refer to the quality of one's capacity to mentally elaborate and open up to his or her subjective experience. The Mental States Task (MST) was developed to evaluate individual differences relative to this capacity. METHOD: Using the MST, participants described a story from an emotionally challenging image and responded to a set of items about their cognitive and emotional processes while completing the task. The validation of the French version of the MST comprises two samples: 264 undergraduate/graduate students with a mean age of 27.27 years (Sample 1), and 206 students with a mean age of 26.61 years (Sample 2). The validation of the English version of the MST also includes two samples: 110 undergraduate students with a mean age of 20.15 years (Sample 3) and 188 students with a mean age of 20.90 years (Sample 4). RESULTS: Results suggest that 6 mental states can be distinguished and that the MST presents an adequate factorial structure, in both its French and English versions. The MST scores were associated with mental state scores derived from a content analysis method and with other related constructs (e.g., authenticity, empathy). CONCLUSIONS: Overall, findings provide convincing evidence of validity and reliability for the MST as an assessment tool of mental states. This innovative measure is likely to facilitate the clinical and empirical investigation of mentalization.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".