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Record W3006138559 · doi:10.1080/14779757.2020.1717986

Working with Transgender and Gender Diverse Clients in Emotion Focused Therapy: Targeting Minority Stress

2020· article· en· W3006138559 on OpenAlexaff
Robin Westmacott, Christopher Edmondstone

Bibliographic record

VenuePerson-Centered & Experiential Psychotherapies · 2020
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of WindsorUniversity of Manitoba
Fundersnot available
KeywordsTransgenderMinority stressPsychologyIntervention (counseling)PopulationStress (linguistics)Clinical psychologyTransphobiaMental healthSexual minorityPsychotherapistMedicineSocial psychologySexual orientationPsychiatry

Abstract

fetched live from OpenAlex

Our objective is to introduce therapeutic intervention, in an EFT framework, with gender diverse people. We review research outlining mental health comorbidity, and some of the unique needs of this population. We understand risk and protective factors and intervention targets through the lens of minority stress theory. Proximal minority stress variables – internalized transphobia, expected rejection, and concealment stress – are reviewed. We outline extant and emerging treatments tailored to members of the transgender and gender diverse population experiencing minority stress. We focus specifically on the utility of EFT as a model for treating proximal minority stress variables and their associated underlying emotion schemes. We focus on adapting central EFT chair tasks to meet the unique needs of transgender and gender diverse clients experiencing minority stress. An illustrative example of the implementation of such is provided in the case of a young transfeminine client experiencing minority stress.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.168
GPT teacher head0.342
Teacher spread0.174 · 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 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

Citations8
Published2020
Admission routes1
Has abstractyes

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