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Record W2727486296 · doi:10.1177/0844562117716851

The Hope Research Community of Practice

2017· article· en· W2727486296 on OpenAlexaffvenue
Janet Landeen, Helen Kirkpatrick, Winnifred Doyle

Bibliographic record

VenueCanadian Journal of Nursing Research · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsCompetence (human resources)NursingQualitative researchNursing practicePsychologyMedical educationNursing researchMedicineSociology

Abstract

fetched live from OpenAlex

Background Clinical nurses have multiple challenges in conducting high-quality nursing research to inform practice. Theoretically, the development of a community of practice on nursing research centered on the concept of hope is an approach that may address some of the challenges. Purpose This article describes the delivery and evaluation of a hope research community of practice (HRCoP) approach to facilitate research expertise in a group of advanced practice nurses in one hospital. It addressed the question: Does the establishment of a HRCoP for master's prepared nurses increase their confidence and competence in leading nursing research? Method Using interpretive descriptive qualitative research methodology, five participants were interviewed about their experiences within the HRCoP and facilitators engaged in participant observation. Results At 13 months, only four of the original seven participants remained in the HRCoP. While all participants discussed positive impacts of participation, they identified challenges of having protected time to complete their individual research projects, despite having administrative support to do so. Progress on individual research projects varied. Conclusion Nurse-led research remains a challenge for practicing nurses despite participating in an evidence-based HRCoP. Lessons learned from this project can be useful to other academic clinical partnerships.

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)0.109
metaresearch head score (Gemma)0.113
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1090.113
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0340.004
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.008
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 head0.767
GPT teacher head0.727
Teacher spread0.040 · 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; both teacher heads agree on what is shown here.

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

Citations16
Published2017
Admission routes2
Has abstractyes

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