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Using the Ottawa Model of Research Use to Implement a Skin Care Program

2004· review· en· W2312421261 on OpenAlexaffabout
Kathleen Graham, Jo Logan

Bibliographic record

VenueJournal of Nursing Care Quality · 2004
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsQueensway-Carleton HospitalUniversity of Ottawa
Fundersnot available
KeywordsFormative assessmentBest practiceClinical PracticeMEDLINEProgram evaluationMedicineNursingMedical educationProcess managementPsychologyBusinessPolitical science

Abstract

fetched live from OpenAlex

Addressing skin care issues requires a systematic and comprehensive approach. We used the pragmatic Ottawa Model of Research Use to guide the implementation of clinical practice guidelines in a surgical program of a tertiary care hospital. Assessments were made of existing clinical practice guidelines, the practice environment, and the potential adopters. With this information, we tailored strategies to address the barriers and to implement the guidelines. A formative evaluation demonstrated positive results.

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)0.335
metaresearch head score (Gemma)0.385
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.335
Threshold uncertainty score0.820

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3350.385
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0160.016
Science and technology studies0.0060.008
Scholarly communication0.0130.010
Open science0.0080.009
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.965
GPT teacher head0.852
Teacher spread0.113 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations78
Published2004
Admission routes2
Has abstractyes

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