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Record W2013111416 · doi:10.5539/ass.v5n11p39

Development of a New Resilience Scale: The Resilience in Midlife Scale (RIM Scale)

2009· article· en· W2013111416 on OpenAlexvenueno aff
Linda Ryan, M. L. Caltabiano

Bibliographic record

VenueAsian Social Science · 2009
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsScale (ratio)PsychologyPsychological resilienceResilience (materials science)PopulationReliability (semiconductor)PsychometricsAdaptation (eye)GerontologyClinical psychologyDevelopmental psychologyDemographySocial psychologyGeographyMedicineCartographySociology

Abstract

fetched live from OpenAlex

Resilience, the ability to maintain or regain positive levels of functioning despite adversity, is one of several strengths that can assist people in positive life adaptation. Midlife (35 - 60 years) is a period when individuals need to adapt to several major changes and challenges. However, no scale exists to measure resilience specifically in the midlife population. Therefore, this study develops a new scale to measure resilience in midlife. The RIM scale consists of 25 items, each self-rated on a 5-point scale (0-4), with higher scores reflecting greater resilience. The scale was administered to a sample of 130 men and women, aged 35 - 60 years, from the normal population. The reliability, validity and factor analytic structure of the scale were evaluated, and reference scores established. The RIM scale demonstrated sound psychometric properties and factor analysis yielded five factors. The RIM scale has potential utility in clinical and research settings.

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)0.002
metaresearch head score (Gemma)0.005
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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.024
GPT teacher head0.386
Teacher spread0.362 · 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
GenreMethods

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

Citations87
Published2009
Admission routes1
Has abstractyes

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