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Record W2346700882 · doi:10.1017/cbo9781139424745

Re-Visioning Psychiatry

2015· book· en· W2346700882 on OpenAlexaff

Bibliographic record

VenueCambridge University Press eBooks · 2015
Typebook
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsMcGill University
Fundersnot available
KeywordsReductionismMental healthPhenomenology (philosophy)Global mental healthPsychologyCultural neuroscienceSociologyEpistemologyPsychotherapistPsychiatryCognition

Abstract

fetched live from OpenAlex

Re-Visioning Psychiatry explores new theories and models from cultural psychiatry and psychology, philosophy, neuroscience and anthropology that clarify how mental health problems emerge in specific contexts and points toward future integration of these perspectives. Taken together, the contributions point to the need for fundamental shifts in psychiatric theory and practice: • Restoring phenomenology to its rightful place in research and practice • Advancing the social and cultural neuroscience of brain-person-environment systems over time and across social contexts • Understanding how self-awareness, interpersonal interactions, and larger social processes give rise to vicious circles that constitute mental health problems • Locating efforts to help and heal within the local and global social, economic, and political contexts that influence how we frame problems and imagine solutions. In advancing ecosystemic models of mental disorders, contributors challenge reductionistic models and culture-bound perspectives and highlight possibilities for a more transdisciplinary, integrated approach to research, mental health policy, and clinical practice.

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)0.004
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: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.034
Scholarly communication0.0090.014
Open science0.0010.008
Research integrity0.0040.013
Insufficient payload (model declined to judge)0.0090.003

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 head0.044
GPT teacher head0.233
Teacher spread0.189 · 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
GenreOther

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

Citations96
Published2015
Admission routes1
Has abstractyes

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