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Record W2312528374 · doi:10.1177/1527154414544965

Use of Modified Delphi to Plan Knowledge Translation for Decision Makers: An Application in the Field of Advanced Practice Nursing

2014· article· en· W2312528374 on OpenAlex
Nancy Carter, John N. Lavis, Sandra MacDonald‐Rencz

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolicy Politics & Nursing Practice · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsHealth CanadaMcMaster University
Fundersnot available
KeywordsDelphi methodDisseminationDelphiContext (archaeology)Knowledge translationKey (lock)Knowledge managementProcess (computing)NursingPublic relationsPlan (archaeology)Face (sociological concept)Medical educationPsychologyMedicineBusinessComputer sciencePolitical scienceSociology

Abstract

fetched live from OpenAlex

Disseminating research to decision makers is difficult. Interaction between researchers and decision makers can identify key messages and processes for dissemination. To gain agreement on the key findings from a synthesis on the integration of advanced practice nurses, we used a modified Delphi process. Nursing decision makers contributed ideas via e-mail, discussed and clarified ideas face to face, and then prioritized statements. Sixteen (89%) participated and 14 (77%) completed the final phase. Priority key messages were around access to care and outcomes. The majority identified "NPs increase access to care" and "NPs and CNSs improve patient and system outcomes" as priority messaging statements. Participants agreed policy makers and the public were target audiences for messages. Consulting with policy makers provided the necessary context to develop tailored policy messages and is a helpful approach for research dissemination.

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.

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)0.004
metaresearch head score (Gemma)0.054
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.287
GPT teacher head0.583
Teacher spread0.295 · 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