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Record W2326160529 · doi:10.1097/nur.0b013e31825aeb80

Energize Staff to Create a Research Agenda

2012· article· en· W2326160529 on OpenAlexaff
Susan A. Bethel, Sue Seitz, Cathie Osika Landreth, Lynette M Gibson, John J. Whitcomb

Bibliographic record

VenueClinical Nurse Specialist · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsGibson Energy (Canada)
Fundersnot available
KeywordsDelphi methodExcellenceScope (computer science)Work (physics)Foundation (evidence)NursingNursing researchInstitutionPublic relationsPolitical scienceMedical educationPsychologyMedicineComputer scienceEngineering

Abstract

fetched live from OpenAlex

There is a need to explore and align priorities for building the foundation for nursing research in any institution. The Delphi technique was chosen as means of setting priorities for nursing research. This method enlisted feedback from all levels of nurses from various practice areas and various nursing roles. A series of 3 rounds of surveys provided feedback. Mixed methods were utilized to reach consensus on the various themes/topics that emerged in each round. In the final survey round, nurses ranked the final themes as to the level of importance, which resulted in identification of the key top 5 priorities. The priorities are the foundation for a research agenda for the coming years. The nursing research council, led by the clinical nurse specialist chairperson, reviewed the results and generated a comprehensive review of literature for current evidence on each priority topic. Evidence was critiqued, rated, and resulted in research questions that formulated the research agenda. The priorities were integrated within the pillars of excellence that are the foundation of the institutions' strategic goals. Using this technique provides a beneficial and structured way to gain input from those who need to own the work of building nursing research within an institution. Positive factors, such as beginning with input from all levels, the ease of providing feedback, and using a Delphi method study to bring alignment within an organization, serve to strengthen nursing research into a much broader scope and focus.

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)0.022
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.005

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 head0.555
GPT teacher head0.646
Teacher spread0.091 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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