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Record W2809361872 · doi:10.1177/2377960818775433

A Sustainability Oriented and Mentored Approach to Implementing a Fall Prevention Guideline in Acute Care Over 2 Years

2018· article· en· W2809361872 on OpenAlexaffabout
Jenny Ploeg, Sandra Ireland, Karen Cziraki, Melissa Northwood, Aleksandra Zecevic, Barbara Davies, Mary Murray, Kathryn Higuchi

Bibliographic record

VenueSAGE Open Nursing · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of OttawaWestern UniversitySt. Joseph’s Healthcare HamiltonOttawa HospitalCambridge Memorial HospitalMcMaster University
Fundersnot available
KeywordsGuidelineSustainabilityNursingMedicineAcute careFocus groupFall preventionHealth careHuman factors and ergonomicsPoison controlMedical emergencyBusinessPolitical science

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the impact of a mentored guideline implementation (Registered Nurses' Association of Ontario Prevention of Falls and Falls Injuries in the Older Adult Best Practice Guideline) focused on enhancing sustainability in reducing fall rates and number of serious falls and the experience of staff in three acute care hospitals. The National Health Service (NHS) Sustainability Model was used to guide the study. Interviews and focus groups were held with 82 point-of-care professional staff, support staff, volunteers, project leaders, clinical leaders, and senior leaders. Study results supported the importance of the factors in the NHS model for sustainability of the guideline in these practice settings. There were no statistically significant decreases in the overall fall rate and number of serious falls. The results supported strategies of participating hospitals to become senior friendly organizations and provided opportunities to enhance staff collaboration with patients and families.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.217
GPT teacher head0.652
Teacher spread0.434 · 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 designQualitative
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

Citations18
Published2018
Admission routes2
Has abstractyes

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