Bibliographic record
Abstract
Clinical Implications of Personality for Mental Health Practice explores the importance of personality traits that shape all forms of psychopathology seen by mental health clinicians. Patients in mental health settings can have problematic personality traits or a diagnosable mental disorder or personality disorder, many of which do not respond to standard treatment. The author argues that taking personality profiles into account is essential to understanding why people have variations in emotion, cognition, and behavior. The chapters review a wide range of research on personality within a broad biopsychosocial context, including interactions between genetics, neural networks, positive and negative life experiences, and resilience. The book shows how personality profiles (using the Five Factor Model) are important for understanding a wide range of mental health conditions, and reviews the biopsychosocial model, applying its theory to personality development. It argues that certain personality traits can raise the risk of mental and personality disorders, examines how these conditions can be diagnosed, and discusses practical applications of personality theory to clinical work. Case vignettes illustrate how therapists can apply an evaluation of personality trait profiles to individualize treatment and help their patients, and—to a certain extent—modify personality. This book is essential for psychiatrists, clinical psychologists, and social workers, as well as students in these fields.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".