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Record W1999746734 · doi:10.1097/nna.0000000000000151

Promoting Evidence-Based Practice Through a Research Training Program for Point-of-Care Clinicians

2014· article· en· W1999746734 on OpenAlexfundaboutno aff
Agnes Black, Lynda G. Balneaves, Candy Garossino, Joseph H. Puyat, Hong Qian

Bibliographic record

VenueJONA The Journal of Nursing Administration · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
FundersMichael Smith Health Research BC
KeywordsExcellenceEvidence-based practiceIntervention (counseling)Health careMedical educationMedicineNursingFocus groupBest practiceTraining (meteorology)PsychologyAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The purpose of this study was to evaluate the effect of a research training program on clinicians' knowledge, attitudes, and practices related to research and evidence-based practice (EBP). BACKGROUND: EBP has been shown to improve patient care and outcomes. Innovative approaches are needed to overcome individual and organizational barriers to EBP. METHODS: Mixed-methods design was used to evaluate a research training intervention with point-of-care clinicians in a Canadian urban health organization. Participants completed the Knowledge, Attitudes, and Practice Survey over 3 timepoints. Focus groups and interviews were also conducted. RESULTS: Statistically significant improvement in research knowledge and ability was demonstrated. Participants and administrators identified benefits of the training program, including the impact on EBP. CONCLUSIONS: Providing research training opportunities to point-of-care clinicians is a promising strategy for healthcare organizations seeking to promote EBP, empower clinicians, and showcase excellence in clinical research.

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)0.019
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0030.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.605
GPT teacher head0.673
Teacher spread0.068 · 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.

Study designObservational
DomainMethods
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

Citations145
Published2014
Admission routes2
Has abstractyes

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