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Record W2603227100 · doi:10.1177/1073191117700269

Assessing the Factor Structure of the Integrative Hope Scale

2017· article· en· W2603227100 on OpenAlexaff
Donald Sharpe, Jesse McElheran, William J. Whelton

Bibliographic record

VenueAssessment · 2017
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsUniversity of AlbertaUniversity of Regina
Fundersnot available
KeywordsPsychologyScale (ratio)PsychoanalysisCognitive psychologyClinical psychologyCartography

Abstract

fetched live from OpenAlex

While some researchers contend that hope is unidimensional, other researchers regard hope to be multidimensional. Schrank, Woppmann, Sibitz, and Lauber's exploratory factor analysis of their Integrative Hope Scale (IHS) found subscales of Trust, Future Orientation, Social Relations, and Lack of Perspective. However, subsequent articles have utilized only the total IHS score. To resolve this issue, a community sample of 288 participants completed the IHS as well as two measures of hedonic well-being (Positive and Negative Affect Schedule; Temporal Satisfaction With Life Scale), a measure of eudemonic well-being (Measure of Actualization of Potential), and a measure of time orientation (the Zimbardo Time Perspective Inventory). One-factor, four-factor oblique, higher order, and bifactor models were compared through confirmatory factor analysis and interpreted using Omega reliability coefficients. While the poorest model fit was for the one-factor model, little reliable variance was found in subscale scores after controlling for a general hope factor with the exception of the Lack of Perspective factor. IHS total and subscale scores were associated with measures of well-being and time orientation. We suggest researchers continue to focus on using the IHS total score, but also report subscale scores, especially for the Lack of Perspective subscale.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.290
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.025
GPT teacher head0.402
Teacher spread0.378 · 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 teacher head, 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

Citations6
Published2017
Admission routes1
Has abstractyes

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