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Record W6945940904 · doi:10.25946/19365107

Barriers to and facilitators of research utilization in practice as seen by a group of perioperative nurses in New Brunswick

2022· dissertation· en· W6945940904 on OpenAlexaboutno aff

Bibliographic record

VenueCentral Queensland University · 2022
Typedissertation
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsData collectionSample (material)PerioperativeResearch designDescriptive researchDescriptive statisticsPublic hospital

Abstract

fetched live from OpenAlex

This comparative descriptive study was conducted at a public regional hospital located in the south-east region of the province of New Brunswick, Canada. The purpose of this study was to identify if the barriers to and facilitators of research utilization in a group of perioperative nurses are similar to other research areas utilizing the same scale.<br>The data collection process was done over a two (2) month period utilizing the Barriers and Facilitators to Using Research in Practice questionnaire (BARRIERS Scale) developed by Funk et al. (1991). The fmal sample size consisted of 46 nurses (61% response rate) who completed the survey.<br>The results show that the top barrier was 'The nurse does not feel she/he has enough authority to change patient care procedures' followed by 'physicians will not cooperate with implementation'. Seven out of the top ten barriers were related to the `setting'. The facilitating factors most frequently suggested by the nurses were related to the setting (organization) as well as the models of education to increase their knowledge of research methods and to develop skills in evaluating research findings. These results are congruent with previous findings regarding the barriers to research utilization. The implication of these and other findings are also discussed.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.472
Teacher spread0.406 · 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 teacher head, 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

Citations0
Published2022
Admission routes1
Has abstractyes

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