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Record W4403286881 · doi:10.1080/21501378.2024.2413052

A Meta-Analysis of the Association Between the Hold Me Tight Program and Couples’ Relationship Adjustment

2024· article· en· W4403286881 on OpenAlexaboutno aff
William Bradley McKibben, A. Stephen Lenz, Arianna Alvero

Bibliographic record

VenueCounseling Outcome Research and Evaluation · 2024
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAssociation (psychology)PsychologyClinical psychologyPsychotherapist

Abstract

fetched live from OpenAlex

In this meta-analysis, the authors tested the overall effect of Hold Me Tight (HMT), a relationship education program for couples based in emotionally focused therapy, on couples’ relationship adjustment. Using a detailed search process, we identified and analyzed eight quantitative, single-group comparison studies. Studies included predominantly cisgender, straight couples (N = 310) in dyadic, monogamous relationships from the United States, Canada, and the Netherlands. The eight studies also included evaluations of online and in-person delivery methods, as well as eight-week groups and one- and two-day workshops. Across the studies, we found that participation in HMT was associated with a small, statistically significant effect (d = 0.377) on couples’ self-reported relationship adjustment, lending support that HMT may be an effective program for couples. Few available studies limited our ability to test additional outcomes or explore moderators of the effect, which are key areas for future research.

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)0.014
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.037
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.040
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.003
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 head0.419
GPT teacher head0.558
Teacher spread0.139 · 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 designMeta-analysis
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

Citations1
Published2024
Admission routes1
Has abstractyes

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