Development and psychometric evaluation of the Job Demands in Nursing Scale and Job Resources in Nursing Scale: Results from a national study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".