Mentalization as a common process in treatments for borderline personality disorder: Commentary on the special issue on mentalization in borderline personality disorder.
Bibliographic record
Abstract
Chapman and Dixon-Gordon were invited to write this commentary, they were concerned that they did not know enough about mentalization to make coherent comments on this interesting series. As it turns out, they were among a shrinking minority, as the past decade has witnessed a surge research on mentalization. Work in this field has been pioneered, in no small part, by the authors of the present issue. Collectively, this set of articles provides a useful summary of the state of mentalization research for the uninitiated and makes a compelling case for mentalization as a key translational construct, particularly with regard to borderline personality disorder (BPD). Mentalization deserves attention in further translational research as well as in treatment refinement for BPD and other clinical problems. Future work should also involve the development of effective, objective ways to assess mentalization. Ultimately, the use of translational constructs to loosen the boundaries between evidence-based treatment approaches may help us move toward more refined, accessible, and effective treatment for BPD and other complex mental health problems.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".