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Record W2160585357 · doi:10.5267/j.msl.2013.09.027

The effectiveness of resilience training on life satisfaction among mothers with mentally retarded children

2013· article· en· W2160585357 on OpenAlexvenueno aff
Sahar Mirghobad Khodarahmi, Najmeh Sedrpoushan, Fatemeh Rezaei

Bibliographic record

VenueManagement Science Letters · 2013
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsMentally retardedPsychologyLife satisfactionResilience (materials science)Training (meteorology)Developmental psychologyClinical psychologyPsychotherapistGeography

Abstract

fetched live from OpenAlex

The present study investigates the effectiveness of resilience training on life satisfaction among the mothers with mentally retarded children.The method is semi experimental using pretest posttest with control group.Statistical population of research includes elementary mentally retarded student who were enrolled in Najafabad Sareban exceptional school over the period 2012-2013 educational year.Sample group includes 50 subjects who randomly replaced in control and experimental groups.Experimental group members participated in a 10-session resilience training.Finally, both groups completed post-tests where research scales were Diener life satisfaction questionnaire.The data are analyzed by co-variance analysis test.Results are significant and indicates that resilience training is effective on life satisfaction (p<0.05).

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.281
Teacher spread0.266 · 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 designNon-randomized trial
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
Published2013
Admission routes1
Has abstractyes

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