MétaCan
Menu
Back to cohort
Record W2404999148

Implementing wound care guidelines: observations and recommendations from the bedside.

2009· article· en· W2404999148 on OpenAlexaff
Jan Lloyd-Vossen

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsSaskatchewan Polytechnic
Fundersnot available
KeywordsMedicineWound careConfusionBest practiceNursingMEDLINEHealth careClinical PracticeMedical educationIntensive care medicinePsychologyManagement
DOInot available

Abstract

fetched live from OpenAlex

The successful implementation of wound care guidelines requires an appreciation for the frustrations experienced by nurses trying to incorporate these tools into clinical practice. These frustrations or barriers to best wound care practice implementation are examined from the perspective of: 1) the practice environment, which must be understood; 2) the potential adopters, predominantly nurses seeking the best fit between evidence and their clinical practice setting; and 3) the evidence-based innovation created to change wound care practice at the point of care. Barriers identified include lack of available resources, time constraints, prescriptive guidelines that incorrectly assume details of the practice environment, and wound care product confusion. Recommendations to facilitate implementation from the bedside are discussed and include expanding guidelines to incorporate detailed educational content and dissemination strategies that serve to increase relevancy to everyday practice. Additional suggestions include decreasing wound care product confusion by developing standardized, function-based product nomenclature and improving the quality of wound care research to increase nurses' confidence in the evidence and resultant recommendations. Resources currently used to develop guidelines also should be utilized to create accompanying educational material to support the transfer and uptake of knowledge.

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)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.404
GPT teacher head0.472
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 teacher head, not a consensus.

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

Citations15
Published2009
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicClinical practice guidelines implementationFrench-language works237,207