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Record W4367665415 · doi:10.1521/jsyt.2022.41.4.17

The Helping Dolls: Utilizing Single-Session Therapy With Latinx Clients

2022· article· en· W4367665415 on OpenAlexvenueno aff
Leo Scaletta, Aimee Fuentez, Stephanie Silva

Bibliographic record

VenueJournal of Systemic Therapies · 2022
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)MindsetAlliancePsychologyPsychotherapistCultural humilityHumilityMedical educationMedicineCultural competencePedagogyComputer sciencePolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The purpose of this article is to discuss the value of the single-session mindset within the Latinx community. We explore the single-session mindset with a composite case of a middle-aged Latinx woman. The given case was a psychotherapy session at Our Lady of the Lake's community clinic where therapists-in-training are supervised by licensed professionals throughout their masters and doctoral programs. The case illustrates working within a single-session framework guided by solution-focused therapy. Additionally, it demonstrates working with a Latina client and what has been traditionally labeled as “posttraumatic stress disorder” (PTSD). The authors further highlight how peoples’ strengths and resources may be utilized to help the client within a single session. A discussion is presented of how such changes were made possible through the use of cultural humility and the establishment of a strong therapeutic alliance, despite the therapists’ initial concerns that language might pose a barrier.

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)0.008
metaresearch head score (Gemma)0.007
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 score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0030.003
Open science0.0030.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.056
GPT teacher head0.321
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

Citations0
Published2022
Admission routes1
Has abstractyes

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