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Record W2499969937 · doi:10.1097/pra.0000000000000161

Psychotherapy and Its Role in Psychiatric Practice: A Position Paper. II. Objective, Subjective, and Intersubjective Science

2016· review· en· W2499969937 on OpenAlexaff
Yakov Shapiro, Nicholas John, ROWAN SCOTT, NADIA TOMY

Bibliographic record

VenueJournal of Psychiatric Practice · 2016
Typereview
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychotherapistPsychologyPosition (finance)Psychiatry

Abstract

fetched live from OpenAlex

In the first article in this 2-part series, we outlined a psychobiological model of psychiatric treatment and reviewed the evidence showing psychotherapy to be a form of biological intervention that induces lasting alterations in brain structure and function. In this second article, we focus on the adaptive model of psychopathology, the effectiveness of psychotherapeutic interventions, the synergistic effects of combined psychotherapy and psychopharmacology treatments, and attention to the patient's subjective experience and the doctor-patient alliance to complement an "objective" case formulation. The evidence strongly suggests the need for an integrated treatment approach based on the objective, subjective, and intersubjective science that forms the foundation of psychiatry as a clinical discipline, in which psychotherapy and psychopharmacology are seen as complementary treatments within a systemic approach to psychiatric care and training. What emerges is the integrated psychobiological model of care with a complex treatment matrix unique to each patient-provider pair and comprised of biological, experiential, and relational domains of treatment which form the foundation of psychiatry as a science of attachment and meaning.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.005
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.403
Teacher spread0.385 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations5
Published2016
Admission routes1
Has abstractyes

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