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Record W1583555940 · doi:10.1002/9781118328910.ch26

Working with Severe and Enduring Eating Disorders: Enhancing Engagement and Matching Treatment to Client Readiness

2012· other· en· W1583555940 on OpenAlexaff
Josie Geller, Suja Srikameswaran, Joanna Zelichowska, Kim Williams

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsAlliancePsychologySection (typography)Eating disordersPsychological resilienceResilience (materials science)Style (visual arts)Empirical researchQuality (philosophy)Matching (statistics)NursingApplied psychologySocial psychologyPsychotherapistPublic relationsClinical psychologyMedicinePolitical scienceBusinessAdvertising

Abstract

fetched live from OpenAlex

This chapter on working with severe and enduring eating disorders (EDs) is organized into four sections. In the first section, a review of research conducted on readiness and motivation for change in the EDs is provided. The empirical literature on barriers to recovery and improving client readiness is described. The second section outlines an alternative model of care for individuals with enduring EDs that is informed by this empirical literature. This model of care emphasizes a ruthless focus on tailoring treatment to client readiness and focusing on quality of life as opposed to recovery. The next section addresses care provider stance, and describes how four care provider styles, or habitual patterns, can negatively impact on the therapeutic alliance. The final section provides an illustration of how to capitalize upon the strength of each style, and a discussion of how care providers working with this group maintain resilience and hope.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.032
GPT teacher head0.298
Teacher spread0.265 · 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".

Quick stats

Citations10
Published2012
Admission routes1
Has abstractyes

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