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Record W2796848792 · doi:10.5737/23688076282110117

Partnership between patients, nurse leaders and researchers: Outcomes of a web-based KT strategy for hospital discharge planning and care transitions in oncology

2018· article· en· W2796848792 on OpenAlexafffundvenueabout
Hélène Lefebvre, Isabelle Brault, Odette Roy, Marie‐Josée Levert, Dan Lecocq, Maryse Larrivière, Michelle Proulx

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationHôpital Maisonneuve-RosemontUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsGeneral partnershipNursingHealth careMedicineWork (physics)Test (biology)BusinessPolitical science

Abstract

fetched live from OpenAlex

A research project brought together patient partners, nurse leaders from six clinical settings in Quebec and researchers to develop and test a web technology, the Forum for Knowledge Exchange (FKE), in order to improve discharge planning practices and oncological care transitions. The project led to the creation of a FKE accessible to the oncology sector of the Francophonie. It revealed an innovative strategy of knowledge transfer (KT) based on the FKE and was fed by collaborative work among partners, where the patient partners played a vital role. The results highlighted the importance, for health research, of giving a voice to patient partners in close collaboration with clinicians and researchers so that clinical practices are better adapted to the actual needs of patients and of their relatives.

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)0.029
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.071
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.083
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0060.003
Open science0.0020.009
Research integrity0.0020.002
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 head0.132
GPT teacher head0.432
Teacher spread0.300 · 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 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

Citations2
Published2018
Admission routes4
Has abstractyes

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Same venueCanadian Oncology Nursing JournalSame topicChronic Disease Management StrategiesFrench-language works237,207