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Record W2618287351 · doi:10.1080/14779757.2017.1330703

Increasing parental self-efficacy with emotion-focused family therapy for eating disorders: a process model

2017· article· en· W2618287351 on OpenAlexaff
Erin J. Strahan, Amanda Stillar, Natasha Files, Patricia Nash, Jennifer Scarborough, Laura Connors, Joanne Gusella, Katherine A. Henderson, Shari Mayman, Patricia Marchand, Emily Orr, Joanne Dolhanty, Adèle Lafrance

Bibliographic record

VenuePerson-Centered & Experiential Psychotherapies · 2017
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsHealth Sciences NorthCape Breton Regional HospitalHotel Dieu HospitalIzaak Walton Killam Health CentreWilfrid Laurier UniversityCanadian Mental Health AssociationLaurentian UniversityUniversity of AlbertaCanadian Psychological Association
Fundersnot available
KeywordsPsychologySelf-efficacyContext (archaeology)Eating disordersIntervention (counseling)Structural equation modelingClinical psychologyBlamePsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

A process model was tested whereby parental fear and self-blame were targeted in order to enhance parental self-efficacy and supportive efforts in the context of emotion-focused family therapy (EFFT) for eating disorders (ED). A 2-day EFFT group intervention was delivered to parents of adolescent and adult children with ED. Data were collected from eight treatment sites (N = 124). Data were analyzed using t-tests, regression analyses and structural equation modeling. The findings supported the proposed process model. Through the processing of parents’ maladaptive fear and self-blame, parents felt more empowered to support their child’s recovery. This increase in self-efficacy led to an increase in parents’ intentions to engage in recovery-focused behaviors. This study is the first to test a method for clinicians to increase supportive efforts by targeting and enhancing caregiver self-efficacy via the processing of emotion.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.062
GPT teacher head0.351
Teacher spread0.289 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations47
Published2017
Admission routes1
Has abstractyes

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