Preparing Nurse Entrepreneurs: The Current State
Bibliographic record
Abstract
Nursing professionals who design and manage a new business venture are known as nurse entrepreneurs. These endeavours provide nurses with the opportunity to develop innovative and creative ways to address gaps in health care. Nurses have a front row seat and observe the changing demands that challenge the healthcare system. Changes include an aging and increasingly culturally diverse population, the rise in chronic illnesses, rapid advancements in technology, increasing costs, and changes in funding. To be successful entrepreneurs requires education in business concepts, precepted graduate experiences, and mentorship opportunities.\nThe purpose of this presentation is to share the results of a literature review that served two purposes. The first purpose was to search the literature on nurse entrepreneurship in Canada and internationally to determine the amount and type of literature focused on this topic; the second was to determine if the business concepts required to be a nurse entrepreneur are being incorporated into nursing undergraduate or graduate nursing curricula.\nThe findings revealed that there is a gap in both research studies and literature on nurse entrepreneurship. Nursing programs did not offer any concentrated tracks in this specialty. Nurses will continue to be ill-equipped to be successful entrepreneurs without changes to education. Research in this area also needs to be encouraged. Nurses must be introduced to entrepreneurial concepts throughout their educational journey. This presentation will offer ideas on how universities can address these 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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".