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Record W2727154840 · doi:10.1111/nin.12212

The impact of regulatory perspectives and practices on professional innovation in nursing

2017· article· en· W2727154840 on OpenAlexaffabout
Sarah Wall

Bibliographic record

VenueNursing Inquiry · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMandateNursingLegislationHealth careNurse educationNursing researchProfessional associationMedicinePublic relationsPolitical science

Abstract

fetched live from OpenAlex

Since at least the 1970s in Canada, there have been calls for health system reforms based on innovative roles and expanded scopes of practice for nurses. Professional regulatory organizations, through legislation, define the standards and parameters of professional nursing practice. Nursing regulators emphasize public protection over the advancement of nursing; regulatory processes and decisions tend to be conservative and risk-averse. This study explored the impact that regulatory processes have on innovation in nursing roles. Nurses in a range of unique practice situations were interviewed, including nurses in non-traditional roles and/or settings, those with cross-jurisdictional career histories, and those working in interdisciplinary practices and educational settings. For these nurses, nursing practice was viewed through a traditional clinical lens, which did not fit for them. They experienced hassle, delay, and inconsistencies in regulatory practices. They felt mistreated and fearful of the regulator and lamented the ways in which ambitious, creative, capable nurses were stymied in attempting new applications for nursing knowledge. Nursing is constraining its own mandate to contribute to health care through stringent licensing processes. Healthcare reform provides an opportunity for nursing regulators to rethink their processes and provide the latitude for nurse-driven change.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.273
GPT teacher head0.617
Teacher spread0.344 · 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.

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

Citations9
Published2017
Admission routes2
Has abstractyes

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