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Record W2737470476 · doi:10.5430/jnep.v7n12p72

The effectiveness of forgiveness therapy for wives of alcoholics

2017· article· en· W2737470476 on OpenAlexvenueno aff
Hee Kyung Kim, Kunsook S. Bernstein

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsForgivenessPsychologyClinical psychologyRepeated measures designAnalysis of varianceTest (biology)Experimental researchAffect (linguistics)MedicineInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

Purpose: The purpose of this study was to examine the effectiveness of forgiveness therapy for wives of alcoholics in South Korea suffering from emotional abuse by their spouses.Methods: Non-randomized quasi-experimental research was conducted with 2-hour weekly forgiveness therapy sessions for 12 weeks, and pre-test, post-test, and a 12-week follow-up test. A total number of 28 subjects were divided into two groups: 15 in the experimental group and 13 in the control group. The data were analyzed by descriptive statistics, t-test, χ2 test, and repeated measure ANOVA, using SPSS 20.0.Results: The experimental group showed a significantly higher score on the forgiveness scale than did the control group (t = 0.312, p < .010) and the 12-week follow-up test (F = 4.43, p = .039). In the subcategories of the forgiveness scale, affect and cognition scores were significantly increased but there was no significant change on the behavior score.Conclusions: These findings suggest that forgiveness therapy may be an effective intervention program to improve forgiveness for the emotionally abused wives of alcoholics.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.106
GPT teacher head0.509
Teacher spread0.402 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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