Nursing intellectual capital theory: operationalization and empirical validation of concepts
Bibliographic record
Abstract
AIMS: To present the operationalization of concepts in the nursing intellectual capital theory and the results of a methodological study aimed at empirically validating the concepts. BACKGROUND: The nursing intellectual capital theory proposes that the stocks of nursing knowledge in an organization are embedded in two concepts, nursing human capital and nursing structural capital. The theory also proposes that two concepts in the work environment, nurse staffing and employer support for nursing continuing professional development, influence nursing human capital. DESIGN: A cross-sectional design. METHODS: A systematic three-step process was used to operationalize the concepts of the theory. In 2008, data were collected for 147 inpatient units from administrative departments and unit managers in 6 Canadian hospitals. Exploratory factor analyses were conducted to determine if the indicator variables accurately reflect their respective concepts. RESULTS: The proposed indicator variables collectively measured the nurse staffing concept. Three indicators were retained to construct nursing human capital: clinical expertise and experience concept. The nursing structural capital and employer support for nursing continuing professional development concepts were not validated empirically. CONCLUSION: The nurse staffing and the nursing human capital: clinical expertise and experience concepts will be brought forward for further model testing. Refinement for some of the indicator variables of the concepts is indicated. Additional research is required with different sources of data to confirm the findings.
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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 | 0.022 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".