Bibliographic record
Abstract
In the last few decades there have been an increasing number of studies on mental health and emotional well-being and their influence on physical health. According to this approach, physical problems often manifest unexpressed hidden inner conflicts. Psychologists increasingly apply their client's awareness of bodily sensations as a tool for therapy. In my paper I would like to present one of such psychological methods named focusing which I use in my practice as a clinical psychologist. Focusing method was elaborated by Eugene Gendlin, American philosopher, who collaborated with the founder of person-centered therapy Carl Rogers. Gendlin’s research showed that positive change in psychotherapy depended on client’s ability to experience bodily reaction of the topics discussed during therapy. In my practice I include Focusing method into my own system based on the idea that we can influence our healing process by discovering inner resources. It becomes possible when we get rid of feeling guilty and feeling of "being wrong" and overcome a negative self-image created as a result of adverse childhood .I have found that the awareness and verbalization of negative feelings facilitate the process of releasing emotional suffering from the memory of the body. Specific bodily responses can lead to discovering the true reasons of emotional conflicts. I would like also to show how I combine the Focusing approach with another psychological method based on the connection of body and mind, Recall Healing elaborated by Canadian expert in holistic approach to health Gilbert Renaud.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".