Exploring mental health professionals’ emotional responses with individuals diagnosed with antisocial personality disorder or psychopathy: a scoping review
Bibliographic record
Abstract
Aims: To conduct a scoping review of primary research studies on clinicians' emotional responses (i.e., countertransference) when working with individuals with antisocial personality and/or psychopathy traits or disorders. A secondary aim is to map clinicians' personal opinions on managing these individuals clinically, as they can influence clinical decision-making. Methods: A PRISMA-ScR compliant scoping review was conducted. PubMed, Web of Science, and EBSCO/PsycINFO were searched for studies published from 01/01/1990 to 06/12/2023, including clinicians providing clinical care to individuals with antisocial or psychopathy traits or disorders assessed via standardized and validated tools and focusing on their cognitive, emotional and behavioral responses, with no restriction regarding context/location and study design (protocol: https://osf.io/eaquj). Results: Twelve studies were included, mostly from Europe, mainly focusing on clinicians working with individuals with ASPD. Only two studies addressed psychopathy. Key findings were organized into four concepts: (a) "common emotional responses," which included nervousness, fear of aggression, detachment, and frustration experienced by the clinician; (b) "willingness to deliver clinical care," indicating the key role of clinicians' confidence in their perceived knowledge and skills to provide effective psychological treatment and highlighting that clinicians working in forensic mental health settings exhibit a higher motivation to provide clinical care compared to those in non-forensic settings; (c) "emotional responses related to misogynistic behaviors," showing additional challenges for assigned female therapists in group therapy; (d) "opinions about clinical management and therapeutic alliance," emphasizing the crucial role of the clinician's respect, recognition, and flexible yet firm boundaries. Conclusion: Individuals with antisocial and psychopathy traits or disorders pose significant challenges in terms of clinicians' emotional responses, highlighting the need for tailored training and supervision to enhance their competence and confidence. The absence of cohort studies and randomized controlled trials on this subject, along with the limited evidence on psychopathy, warrants for further research. Systematic review registration: https://osf.io/eaquj.
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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 | 0.023 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".