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Record W2585391427 · doi:10.1017/9781107298613.021

Improving Emotional Processing

2017· book-chapter· en· W2585391427 on OpenAlexaff
W. John Livesley

Bibliographic record

VenueCambridge University Press eBooks · 2017
Typebook-chapter
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyPsychological interventionPersonalityValue (mathematics)Cognitive psychologyApplied psychologySocial psychologyComputer scienceMachine learning

Abstract

fetched live from OpenAlex

The previous modules sought to improve emotion-regulating skills and strategies. However, skill building alone is not sufficient. It needs to be supplemented by interventions that enhance emotional processing capacity, restore the informational value of emotions, and integrate emotions with other aspects of personality functioning. The term “enhance emotional processing” requires explanation. What we need to build is a more nuanced activation and expression of emotions by: (i) developing greater flexibility in how emotional events are interpreted and managed; (ii) integrating emotions with other mental processes so that behaviour is more coherent; and (iii) constructing higher-order meaning systems and narratives that integrate emotions with other personality processes and coordinate and regulate the way emotions are expressed. Work on enhancing emotional processing begins to change therapy in subtle but important ways. First, less-structured interventions are needed both to restructure emotional schemas that are well-established and central parts of belief systems and to help patients to construct new narratives. Consequently, this chapter deals more with principles than specific interventions. Second, since interpersonal factors loom large in triggering emotion and influencing subsequent action, the focus of treatment becomes increasingly interpersonal and the regulation and modulation phase progressively merges with the exploration and change phase, which is primarily concerned with the interpersonal domain. Enhancing Flexibility in Emotional Responses The rigidity that is a prominent feature of borderline personality disorder (BPD) extends to the expression and processing of emotions. Emotional expression tends to lack flexibility and subtly is partly due to the intensity of emotions – it is difficult to be flexible when feelings are overwhelming – and partly due to the impact of maladaptive schemas that give rise to fixed ways of thinking about and responding to emotional events. Promoting the Idea of Emotion as a Process Rigid emotional reactions are also linked to assumptions that emotions are enduring states as opposed to processes that wax and wane. This assumption is not surprising given the intensity and persistency of emotional states in BPD and patients’ tendency to “fuse” with their emotions and define themselves largely in terms of their current emotional state. It is also maintained by the limited time perspectives of patients who have difficulty integrating events across time and recalling how feelings change with fluctuations in mental state.

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)0.000
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.006

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 head0.046
GPT teacher head0.251
Teacher spread0.206 · 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
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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