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Record W2746285394

RESEARCH REFLECTIONS: Communicating your research

2017· article· en· W2746285394 on OpenAlexaboutno aff
Jennifer Stephens

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge translationDisseminationKnowledge transferHealth careConfusionPsychologyKnowledge managementPublic relationsComputer sciencePolitical science
DOInot available

Abstract

fetched live from OpenAlex

<p class="p1">Several years of Research Reflections have provided instruction and supportive guidance to assist both novice and advanced scholars in conducting and appraising nursing research. From developing a strong research question to critically evaluating the quality of a published study, the ultimate purpose of nursing research is to disseminate findings in order to have an impact on clinical practice. This objective is contained within the notion of knowledge translation (KT). The Canadian Institutes for Health Research (CIHR, 2016) defines KT as “a dynamic and iterative process” consisting of several steps that foster the creation, and subsequent dissemination, of knowledge for the purpose of improving the health of Canadians by strengthening healthcare services. A short list of additional terms imbued with similar purpose and meaning to KT include knowledge exchange, implementation, research utilization, diffusion, and knowledge transfer. Graham and colleagues (2006) suggested that confusion arising from multiple methodologies and theories for disseminating research findings be clarified to ensure that they are not “lost in knowledge translation” (p.13). Indeed, for both novice and experienced researchers an awkward and frustrating disconnect can exist between generated research knowledge and crucial stakeholders it was meant to inform. Unless research results are communicated with others in a way that is effective and meaningful, potentially important and practice-changing knowledge could slip into the obscurity of a file cabinet or rarely-cited manuscript.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScholarly communication
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptMetaresearchScholarly communication
Domain: Reporting · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
models splitAgreement compares identical category sets and study designs across arms.

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)0.071
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0710.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0250.001
Scholarly communication0.0030.005
Open science0.0140.008
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0160.001

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 head0.946
GPT teacher head0.839
Teacher spread0.107 · 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

Labeled directly by 2 models reading the full record.

Scholarly communicationMetaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable
DomainReporting
GenreCommentary

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

Citations0
Published2017
Admission routes1
Has abstractyes

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