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Record W3003178723 · doi:10.26443/ijwpc.v7i1.208

Beyond Psychiatric Symptoms

2020· article· en· W3003178723 on OpenAlexaffvenue
Tewfik Said, Ahmad H. Almadani

Bibliographic record

VenueInternational Journal of Whole Person Care · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsPsychicAnxietyPsychiatryPsychologyPsychological interventionDepression (economics)Set (abstract data type)MedicinePsychotherapistAlternative medicine

Abstract

fetched live from OpenAlex

Psychic pain goes far beyond the set of psychiatric symptoms that afflict our patients. Actually, there is much debate in our field as to what gives rise to the other; is psychic pain a by-product of psychiatric symptoms, such as depression and anxiety, or are symptoms a manifestation of psychic pain, namely that we develop symptoms by virtue that the psychic pain is unbearable. Although many of our therapeutic interventions tend to target symptom removal, or at least their alleviation, fewer efforts are placed on understanding the patients’ psychic pain. During this workshop "Beyond Psychiatric Symptoms", the presenters will give a brief outline of what we know about psychic pain and the challenge that is faced in reaching it. With extensive use of audiovisually recorded clinical interviews, we will expand on these concepts. A special emphasis will be placed on the training of health professionals to be able to identify, tolerate, and work with such pain on a daily basis. As this workshop will present vignettes of actual clinical interviews with patients, any form of recording or taking pictures throughout the presentation is absolutely forbidden to preserve patients’ confidentiality.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.027
GPT teacher head0.275
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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