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Record W4295219636 · doi:10.1016/j.jbtep.2022.101768

The fear of losing control

2022· article· en· W4295219636 on OpenAlexafffund
Adam S. Radomsky

Bibliographic record

VenueJournal of Behavior Therapy and Experimental Psychiatry · 2022
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyPanic disorderPsychopathologyPerspective (graphical)AnxietyCognitionSocial anxietyClinical psychologyAnxiety disorderControl (management)PsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Although a great deal of research has focused on various aspects of control and their relations to psychopathology, new insights and therapeutic potential could be revealed through an examination and perhaps emphasis on fears of losing control. Although elements of control-related beliefs and phenomena have been highlighted in association with obsessive-compulsive disorder and social anxiety disorder, this paper will give a fresh perspective on fears and beliefs about losing control, with implications for understanding and perhaps treating a broad range of psychological problems including panic disorder, posttraumatic stress disorder (PTSD), and others. New research questions and hypotheses are proposed, along with potential implications for expanded research into this domain and for clinical applications within a cognitive-behavioural approach.

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)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.006
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.030
GPT teacher head0.346
Teacher spread0.316 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations28
Published2022
Admission routes2
Has abstractyes

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