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Record W2757404157 · doi:10.1080/01609513.2017.1369922

From Serodiscordant to Magnetic: The Feasibility and Acceptability of a Pilot Psychoeducational Group Intervention Designed to Improve Relationship Quality

2017· article· en· W2757404157 on OpenAlexafffund
Andrew D. Eaton, Jessica Cattaneo, Jocelyn M. Watchorn, Celeste Bilbao-Joseph, Scott Bowler, Michael Hazelton, James C. Myslik, Andrew Ross, Lori Chambers

Bibliographic record

VenueSocial Work With Groups · 2017
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsMcMaster UniversityMount Sinai HospitalAIDS Committee of TorontoEast Wellington Family Health TeamCentre for Social InnovationUniversity of Toronto
FundersOntario HIV Treatment Network
KeywordsSerodiscordantPsychological interventionPsychoeducationPsychologyIntervention (counseling)Clinical psychologySession (web analytics)PsychotherapistPhysical therapyHuman immunodeficiency virus (HIV)MedicinePsychiatryFamily medicineComputer science

Abstract

fetched live from OpenAlex

Serodiscordant or magnetic couples experience HIV-related issues that can compound daily stressors. Psychoeducational couples group interventions can build coping skills and increase relationship satisfaction. Throughout Summer 2014, 6 gay male magnetic couples (n = 12) collaboratively designed and participated in an 8-session psychoeducational support group. The intervention was feasible (i.e., recruitment was <2 weeks, it was easy to coordinate) and acceptable (i.e., each session was consistently rated very good or outstanding). Relationship quality improved significantly in all couples regardless of whether couples agreed on how to manage HIV within their relationship. Implementation of this model is encouraged to fully evaluate this promising intervention.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.079
GPT teacher head0.437
Teacher spread0.359 · 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 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

Citations7
Published2017
Admission routes2
Has abstractyes

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