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Record W4413834279 · doi:10.1080/15332691.2025.2545877

Coming Together in Crisis: Development and Implementation of Developmental Couple Therapy for Complex Trauma Virtual Groups for Couples During COVID

2025· article· en· W4413834279 on OpenAlexaff
Heather B. MacIntosh, Sarah McNamee

Bibliographic record

VenueJournal of Couple & Relationship Therapy · 2025
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Psychology2019-20 coronavirus outbreakPsychoanalysisSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychotherapistDevelopmental psychologyMedicineVirology

Abstract

fetched live from OpenAlex

This study explored the development, implementation and feasibility of a manualized online group therapy program for couples struggling with the intersecting impacts of childhood trauma, marginalization, and the pandemic based on the first two stages of Developmental Couple Therapy for Complex Trauma (DCTCT). Qualitative data included session observations, participant feedback, and therapist feedback. Qualitative findings highlight that DCTCT virtual groups were experienced as helpful and of high relevance to participants. DCTCT virtual groups for couples offer a feasible, acceptable, and highly accessible group intervention for trauma-survivor couples and may be most beneficial combined with subsequent couple therapy focused on trauma-processing and strengthening relational skills.

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)0.007
metaresearch head score (Gemma)0.010
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.060
GPT teacher head0.418
Teacher spread0.357 · 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
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
Published2025
Admission routes1
Has abstractyes

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