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Record W2884046157 · doi:10.1177/0844562118790714

Inner Strength—State of the Science

2018· review· en· W2884046157 on OpenAlexvenueno aff
C. Shaler Smith, Catherine Dingley, Gayle Roux

Bibliographic record

VenueCanadian Journal of Nursing Research · 2018
Typereview
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsInner coreInner cityRange (aeronautics)Measure (data warehouse)Strength theoryComputer scienceEngineeringPhysicsEconomic geographyData miningEconomicsCompressive strengthAerospace engineeringThermodynamicsGeophysics

Abstract

fetched live from OpenAlex

The phenomenon of inner strength has been investigated since 1990. Foundations for inner strength have been developed using inductive designs that have led to a middle-range theory of inner strength and a conceptual model. Two instruments have been developed to measure and test inner strength and have been predominantly used in samples of women and the elderly. This paper will trace the development of inner strength knowledge from two distinct paradigms in North America and Northern Europe. Empirical testing is described. Gaps in the current state of knowledge of inner strength are identified as well as implications for future research.

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)0.018
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0030.004
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0000.000

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 head0.421
GPT teacher head0.630
Teacher spread0.209 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations22
Published2018
Admission routes1
Has abstractyes

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