Nursing intellectual capital theory: testing selected propositions
Bibliographic record
Abstract
AIMS: To test the selected propositions of the middle-range theory of nursing intellectual capital. BACKGROUND: The nursing intellectual capital theory conceptualizes nursing knowledge's influence on patient and organizational outcomes. The theory proposes nursing human capital, nurses' knowledge, skills and experience, is related to the quality of patient care and nurse recruitment and retention of an inpatient care unit. Two factors in the work environment, nurse staffing and employer support for nurse continuing professional development, are proposed to influence nursing human capital's association with patient and organizational outcomes. DESIGN: A cross-sectional survey design. METHODS: The study took place in 2008 in six Canadian acute care hospitals. Financial, human resource and risk data were collected from hospital departments and unit managers. Clearly specified empirical indicators quantified the study variables. The propositions of the theory were tested with data from 91 inpatient care units using structural equation modelling. RESULTS: The propositions associated with the nursing human capital concept were supported. The propositions associated with the employer support for nurse continuing professional development concept were not. The proposition that nurse staffing's influences on patient outcomes was mediated by the nursing human capital of an inpatient unit, was partially supported. CONCLUSION: Some of the theory's propositions were empirically validated. Additional theoretical work is needed to refine the operationalization and measurement of some of the theory's concepts. Further research with larger samples of data from different geographical settings and types of hospitals is required to determine if the theory can withstand empirical scrutiny.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".