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Record W3004235279 · doi:10.33824/pjpr.2019.34.4.36

Protective Factors for Subjective Well-being in Mothers of Children with Down Syndrome

2020· article· en· W3004235279 on OpenAlexaff
Iram Fatima, Kausar Suhail

Bibliographic record

VenuePakistan Journal of Psychological Research · 2020
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsBC Mental Health & Substance Use Services
Fundersnot available
KeywordsPsychologySocial supportMoodDevelopmental psychologyScale (ratio)Life satisfactionClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

The study was conducted to understand the relationship of general self-efficacy and two aspects of social support with cognitive and affective facets of subjective well-being in mothers of children having Down syndrome in contrast to mothers of typical children. Survey was conducted with mothers of two types of children (n = 89 each). Data were collected through Generalized Self-Efficay Scale (Schwarzer & Jerusalem, 1995), Social Support Questionnaire-Short Form (SSQ-6; Sarason, Sarason, Shearin, & Pierce, 1987), and Trait Well-Being Inventory (Dalbert, 1992). It was found that with higher level of perceived available social support, the mothers of children having Down syndrome were more satisfied with their life. Further, with higher self-efficacy and higher satisfaction with the social support, mothers of both types of children were more satisfied with their life and had better mood in general. Perceived available social support benefitted mothers of children having Down syndrome only, while, satisfaction with social support and self-efficacy were protective factors for subjective well-being of mothers, in general.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.127
GPT teacher head0.464
Teacher spread0.337 · 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
Published2020
Admission routes1
Has abstractyes

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