Use of empathy in psychiatric practice: Constructivist grounded theory study
Bibliographic record
Abstract
BACKGROUND: (DSM) and medications with purported disregard for empathetic, humanistic interventions. AIMS: To develop an empirically based qualitative theory explaining how psychiatrists use empathy in day-to-day practice, to inform practice and teaching approaches. METHOD: This study used constructivist grounded theory methodology to ask (a) 'How do psychiatrists understand and use empathetic engagement in the day-to-day practice of psychiatry?' and (b) 'How do psychiatrists learn and teach the skills of empathetic engagement?' The authors interviewed 17 academic psychiatrists and 4 residents and developed a theory by iterative coding of the collected data. RESULTS: This constructivist grounded theory of empathetic engagement in psychiatric practice considered three major elements: relational empathy, transactional empathy and instrumental empathy. As one moves from relational empathy through transactional empathy to instrumental empathy, the actions of the psychiatrist become more deliberate and interventional. CONCLUSIONS: Participants were described by empathy-based interventions which are presented in a theory of 'empathetic engagement'. This is in contrast to a paradigm that sees psychiatry as purely based on neurobiological interventions, with psychotherapy and interpersonal interventions as completely separate activities from day-to-day psychiatric practice. DECLARATION OF INTEREST: None. COPYRIGHT AND USAGE: © The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license.
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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 | 0.065 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".