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Canadian Master Couple Therapists

2016· book-chapter· en· W4240157303 on OpenAlexaboutno aff
Allyson Smith, William J. Whelton

Bibliographic record

VenueOxford University Press eBooks · 2016
Typebook-chapter
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisPsychologyNarrativeNOMINATEMedical educationQualitative researchProfessional developmentPedagogyMedicineSociology

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the characteristics, skills, and experiences of master couple therapists and to gain a deeper understanding of how these experts approach the specialized practice of couple therapy. Seven psychologists and two social workers designated by their professional colleagues as “master couple therapists” participated in a qualitative interview and wrote narratives about their skills, characteristics, and experiences. Individuals who nominated these “master couple therapists” were also interviewed to further understand their choice to nominate these particular practitioners. Data from master couple therapists and nominator participants were analyzed using a category construction and thematic analysis process. Three overarching themes emerged from the data and suggested that this group of master couple therapists demonstrated a remarkable commitment to personal development and self, professional development, and relationships. The importance of engaging in self-care, ongoing learning, and developing strong personal and professional relationships is explored.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.719
Threshold uncertainty score0.559

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0630.006

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 head0.026
GPT teacher head0.220
Teacher spread0.194 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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