Patient Psychopathology and the Management of Clinical Dilemmas in Psychotherapy: A Qualitative Analysis of Clinical Decision-Making
Bibliographic record
Abstract
Introduction: Clinical dilemma management is an important part of daily decision-making processes in psychotherapy, and hence important for the quality of mental healthcare. However, the situated particularities of such dilemmas have been given little systematic attention – both in research and in practice, even though an improved understanding of the nature of clinical dilemmas is a central key to managing dilemmas successfully. Method: Eight cases of authentic clinical dilemma management in psychotherapy have been analysed from the perspective of interaction analysis and psychopathology. The article uses video data and narrative interviews from a larger cognitive ethnography study conducted at a psychiatric Hospital in Denmark. Results: The analysis demonstrates how clinical dilemma management in psychotherapy is particularly difficult due to the nature of a patient’s psychopathology. Thus, it is often difficult to discern whether a given dilemma is intrinsically ethical, or if it is a manifestation of the patient’s pathology. Two overall interaction patterns were identified: In the first pattern, the therapist fails to manage the clinical decision-making in accordance with the therapeutic goal, which strengthens the patient’s psychopathological behaviour, for instance by giving in and do what the patient demands. In the second pattern, the therapist uses the situation as an opportunity to work with the patient’s psychopathological behaviour in situated interaction. Conclusion: This article presents a model for integrating an understanding of patient pathology into clinical and ethical decision-making. It establishes a window into how psychotherapists manage clinical dilemmas (successfully or not) through interaction. This illustration might impact on how we address, evaluate and understand clinical and ethical dilemma management, which again can contribute to the reduction of moral distress amongst healthcare practitioners, as well as amongst patients.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".