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Record W1967352344 · doi:10.5737/1181912x2213135

Mentoring advanced practice nurses in research: Recommendations from a pilot program

2011· article· en· W1967352344 on OpenAlexafffundvenueabout
Doris Y. P. Leung, Kimberley Widger, Doris Howell, Sioban Nelson, Alex Molassiotis

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsPrincess Margaret Cancer CentreHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick ChildrenUniversity of Toronto
KeywordsMentorshipAdvanced Practice NursesMedical educationDoctor of Nursing PracticeAcademic institutionPlan (archaeology)MedicineTranslational researchNursing practiceNursingNurse educationManagementPolitical scienceHealth care

Abstract

fetched live from OpenAlex

Advanced Practice Nurses (APNs) need research skills to develop and advance their practice and, yet, many have limited access to research training and support following completion of their advanced degree. In this paper we report on the development, delivery, and evaluation of an innovative pilot program that combined research training and one-to-one mentorship for nine APNs in conducting research relevant to their practice. The program was organized within an academic institution and its affiliated hospitals in Toronto, Canada. Our experience with this program may assist those in other organizations to plan and deliver a similar program for APN research mentorship.

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)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.656
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
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 head0.619
GPT teacher head0.651
Teacher spread0.032 · 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 designNot applicable
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

Citations8
Published2011
Admission routes4
Has abstractyes

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