Phenomenological Empathy and the Professional Role in Recovery-Oriented Practice
Bibliographic record
Abstract
This paper aims to show how a phenomenological theory of empathy can be used to achieve a close interpersonal relationship that serves to support shared decision making and recovery from mental health problems. This framework can also be seen as a way to maintain a professional distance in such relationships. First, the paper briefly describes the basics of shared decision making and recovery-oriented practice. Second, the paper presents the notion of second-person perspectivity, the “we-relation”, and the phenomenological term epoché as a background to discussing the possibility of performing a specific kind of epoché, which actively brackets taken-for-granted presuppositions and notions and instead facilitates a focus on the meaning of the other’s experience: a special kind of intentionality directed toward the other’s intentionality. Third, the paper notes that the aim of actively assuming an empathic attitude paves the way for a passive ethnographic epoché that allows for an exploration of the other’s personal world, which constitutes the context for meaning. In this way, we can increase the possibilities of developing a professional “we-relation” and minimizing the risk of emotional contagion. This is a skill that can be learned through training, and that can increase the possibility of developing a deeper interpersonal understanding that will be of value to recovery-oriented practice.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".