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Success Indicators and Barriers to Acute Nurse Practitioner Role Implementation in Four Ontario Hospitals

2001· article· en· W2022448677 on OpenAlexaffabout
Mary H. van Soeren, Vaska Micevski

Bibliographic record

VenueAACN Clinical Issues Advanced Practice in Acute & Critical Care · 2001
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsWestern University
Fundersnot available
KeywordsAcute careNursingMentorshipCLARITYHealth careMedicineSpecialtyFamily medicineMedical education

Abstract

fetched live from OpenAlex

Changes in healthcare environmental factors resulted in the introduction of the acute care nurse practitioner (ACNP) role in Ontario. The purpose of the study was to identify success indicators, barriers, and recommendations for role implementation to assist healthcare providers to develop strategies for integrating ACNPs into teams. Acute care nurse practitioners (n = 14), physicians (n = 14), administrators (n = 12), and staff nurses (n = 48) from four tertiary care hospitals completed a researcher-developed, self-administered questionnaire with fixed and open-ended questions. Specialty practice areas (cardiac/critical care, geriatrics, and nephrology) were matched within the four sites. Acute care nurse practitioners (n = 14), physicians (n = 12), administrators (n = 8), and staff nurses (n = 34) responded. The major indicator by all groups for successful role implementation was level of preparation. Barriers included lack of mentorship and knowledge of the role, and perceived lack of support from administration and physicians. Themes reflecting impact on patient care were improved communication and attention to patient care issues. Respondents accepted the role, concluding that enhanced continuity of care was a result. Role clarity before and during implementation would assist team members in understanding the purpose and value of the role, thus easing the integration of the ACNP into the healthcare team.

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)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.004
Open science0.0000.000
Research integrity0.0010.003
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 head0.037
GPT teacher head0.552
Teacher spread0.515 · 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

Citations71
Published2001
Admission routes2
Has abstractyes

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