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Record W2048778683 · doi:10.12927/cjnl.2013.23450

Nursing Entrepreneurship: Motivators, Strategies and Possibilities for Professional Advancement and Health System Change

2013· article· en· W2048778683 on OpenAlexaffvenueabout
Sarah Wall

Bibliographic record

VenueNursing leadership · 2013
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRestructuringNursingEntrepreneurshipWork (physics)Organizational changeHealthcare systemProfessional developmentHealth carePsychologyBusinessMedicinePublic relationsPolitical scienceMedical education

Abstract

fetched live from OpenAlex

In Canada, as well as internationally, efficiency-focused organizational restructuring in healthcare has resulted in stressful job change for nurses, although nurses continue to work in a system that values technology-based, physician-provided services. Employed nurses have had to participate in organizational activities that undermine their professional values and goals. Nursing entrepreneurship presents an opportunity to explore nursing's professional potential in nursing practice that is uniquely independent. In this study, a focused ethnographic approach was used to explore the experiences of self-employed nurses, who see themselves as leaders in advancing the profession of nursing and its contribution to healthcare. Key themes in the findings include the responses of self-employed nurses to health system change, expanded roles for nurses, the consequences of this non-traditional approach to nursing work and the possibilities for change that arise from nursing entrepreneurship. This research has implications for healthcare policy, professional advocacy and nursing education.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.475
GPT teacher head0.497
Teacher spread0.022 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2013
Admission routes3
Has abstractyes

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