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Record W2901396837 · doi:10.1002/nop2.215

Development and psychometric evaluation of the Job Demands in Nursing Scale and Job Resources in Nursing Scale: Results from a national study

2018· article· en· W2901396837 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNursing Open · 2018
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalSaskatchewan Health AuthorityHôpital Maisonneuve-RosemontUniversity of LethbridgeUniversity of Northern British ColumbiaUniversité de MontréalUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsScale (ratio)NursingPsychologyMedicineGeography

Abstract

fetched live from OpenAlex

AIM: To develop and test the psychometric properties of the Job Resources in Nursing (JRIN) Scale and the Job Demands in Nursing (JDIN) Scale. DESIGN: Cross-sectional survey. METHODS: A three-phase process of instrument development and psychometric evaluation was employed: Phase 1: development of a 42-item JRIN Scale and 60-item JDIN Scale through extensive literature review, expert consultation and an iterative content evaluation; Phase 2: pilot survey of 89 nurses and use of item discrimination analysis to estimate the internal consistency reliability of each subscale and reduce the length of each scale; Phase 3: Modified scales were tested in a nationwide survey of 3,822 rural/remote nurses, including use of exploratory factor analysis. RESULTS: The 24 items related to job resources favoured a six-factor structure, accounting for 63% of the variance, Cronbach's alpha 0.88. The 22 items related to job demands favoured a six-factor structure, accounting for 59% of the variance, Cronbach's alpha 0.84.

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.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.069
GPT teacher head0.410
Teacher spread0.340 · 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