MétaCan
Menu
Back to cohort
Record W2563424689 · doi:10.1177/084456211404600203

Knowledge Transfer and Dissemination of Advanced Practice Nursing Information and Research to Acute-Care Administrators

2014· article· en· W2563424689 on OpenAlexaffvenue
Nancy Carter, Maureen Dobbins, Gladys Peachey, Heather Hoxby, Sandra Ireland, Noori Akhtar‐Danesh, Alba DiCenso

Bibliographic record

VenueCanadian Journal of Nursing Research · 2014
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsInformation transmissionNursingContext (archaeology)PsychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

The objective of this study was to ascertain the information needs and knowledge-dissemination preferences of acute-care administrators with respect to advanced practice nursing (APN). Supportive leadership is imperative for the success of APN roles and administrators need up-to-date research evidence and information, but it is unclear what the information needs of administrators are and how they prefer to receive the information. A survey tool was developed from the literature and from the findings of a qualitative study with acute-care leaders. Of 107 surveys distributed to nursing administrators in 2 teaching hospitals, 79 (73.8%) were returned. Just over half of respondents reported wanting APN information related to model of care and patient and systems outcomes of APN care; the majority expressed a preference for electronic transmission of the information. Researchers need multiple strategies for distributing context-specific APN evidence and information to nursing administrators.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.056
metaresearch head score (Gemma)0.248
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.295

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.248
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.102
GPT teacher head0.566
Teacher spread0.463 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Nursing ResearchSame topicNursing Roles and PracticesFrench-language works237,207