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Record W2982314313 · doi:10.5539/gjhs.v11n12p198

Transforming South African Public Hospitals Through Intrapreneurship Practice: Views of Unit Nurse Managers Regarding Their Potential Contribution

2019· article· en· W2982314313 on OpenAlexvenueno aff
Thandiwe Marethabile Letsie

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Strategy and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsIntrapreneurshipTransformative learningNursingCreativityBureaucracyFocus groupUnit (ring theory)Health carePublic relationsQuality (philosophy)BusinessPsychologyMedicineEntrepreneurshipPolitical scienceMarketingPedagogy

Abstract

fetched live from OpenAlex

The insurmountable challenges confronting public hospitals globally jeopardize envisaged quality. South Africa health care system faces a number of challenges calling for strategic business approaches embodied through intrapreneurship practice. As a foreign concept in nursing, intrapreneurship is least understood. Views of unit nurse managers concerning their potential contribution towards improving services at public hospitals are least understood. A qualitative, descriptive and explorative study done through focus groups shed light. Data analysed through Tesch approach culminated in rich verbatim. The participants’ shared the following views concerning their intrapreneurial contribution; ability to take–risk in bureaucratic public hospitals; they associate the initiative with creativity and novelty through leading transformative projects in teams, the business leaders are knowledgeable, and share ideas internally or outside. The remarkable paradigm shift in nursing adopting business strategies has a significant impact on outcome of nursing care. The proposed recommendations adding significant value, transform health care policy, practice, education. On-going capacity development for the nurses in clinical practice is a necessary quality improvement initiative.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.005
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.285
Teacher spread0.263 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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