Examination of self patterns: framing an alternative phenomenological interview for use in mental health research and clinical practice
Bibliographic record
Abstract
Mental disorders are increasingly understood as involving complex alterations of self that emerge from dynamical interactions of constituent elements, including cognitive, bodily, affective, social, narrative, cultural and normative aspects and processes. An account of self that supports this view is the pattern theory of self (PTS). The PTS is a non-reductive account of the self, consistent with both embodied-enactive cognition and phenomenological psychopathology; it foregrounds the multi-dimensionality of subjects, stressing situated embodiment and intersubjective processes in the formation of the self-pattern. Indications in the literature already demonstrate the viability of the PTS for formulating an alternative methodology to better understand the lived experience of those suffering mental disorders and to guide mental health research more generally. This article develops a flexible methodological framework that front-loads the self-pattern into a minimally structured phenomenological interview. We call this framework ‘Examination of Self Patterns’ (ESP). The ESP is unconstrained by internalist or externalist assumptions about mind and is flexibly guided by person-specific interpretations rather than pre-determined diagnostic categories. We suggest this approach is advantageous for tackling the inherent complexity of mental health, the clinical protocols and the requirements of research.
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 | 0.040 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".