Analyzing economic feasibility for investing in nursing care: Evidence from panel data analysis in 35 OECD countries
Bibliographic record
Abstract
OBJECTIVE: To analyze economic feasibility for investing in nursing care. METHOD: The number of practicing nurses' density per 1000 population as a proxy for nursing staff and Gross Domestic Product (GDP) per capita (current US$) were collected in 35 member countries of Organization for Economic Co-operation and Development (OECD) over 2000-2016 period. The statistical technique of panel data analysis including unit root test, cointegration analysis, Granger causality test, dynamic long-run model analysis and error correction model were applied to measure economic impact of nursing-related services. RESULTS: There was a committed bilateral relationship between nurse-staffing level and GDP with long-run magnitudes of 1.39 and 0.41 for GDP-lead-nurse and nurse-lead-GDP directions in OECD countries, respectively. Moreover, the highest long-run magnitudes of the effect nursing staff has on increasing GDP per capita were calculated in Finland (2.07), Sweden (1.92), Estonia (1.68), Poland (1.52), Czech Republic (1.48), Norway (1.47) and Canada (1.24). CONCLUSION: Our findings verify that although the dependency of nursing characteristics to GDP per capita is higher than the reliance of GDP to number of nurses' density per 1000 population, investing in nursing care is economically feasible in OECD countries i.e. nursing is not only a financial burden (or cost) on health care systems, but also an economic stimulus in OECD countries. Hence, we alert governments and policy makers about the risk of underestimating the economic impacts of nurses on economic systems of OECD countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".