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Record W1966548902 · doi:10.12927/hcpol.2007.18876

A Community-Researcher Alliance to Improve Chronic Wound Care

2007· article· fr· W1966548902 on OpenAlexaffvenueabout
Ian D. Graham, Margaret Harrison, Bob Cerniuk, Sheila Bauer

Bibliographic record

VenueHealthcare policy · 2007
Typearticle
Languagefr
FieldMedicine
TopicDiagnosis and Treatment of Venous Diseases
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGeneral partnershipAllianceContext (archaeology)Wound careNursingAgency (philosophy)Public relationsQueen (butterfly)Health careMedicineBusinessPolitical scienceSociologyIntensive care medicine

Abstract

fetched live from OpenAlex

A partnership between health services researchers from Queen's University and the University of Ottawa, a community nursing agency and a home care authority in Ottawa led to major improvements in the quality of care for people with leg ulcers. The synthesis of both external and local evidence played a key role in the adoption of an evidence-based protocol and provided the critical context to support a significant reorganization of the existing service delivery model. This case demonstrates that, with a collaborative partnership approach, systematic and transparent research processes can be rapidly developed to support policy change.

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)0.099
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.099
Threshold uncertainty score0.523

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0990.086
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0160.010
Scholarly communication0.0100.008
Open science0.0030.029
Research integrity0.0110.010
Insufficient payload (model declined to judge)0.0220.003

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 head0.069
GPT teacher head0.442
Teacher spread0.374 · 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.

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

Citations8
Published2007
Admission routes3
Has abstractyes

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