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Record W2030511551 · doi:10.5737/1181912x1611117

Implementing the role of patient-navigator nurse at a university hospital centre

2006· article· en· W2030511551 on OpenAlexafffundvenue
Lise Fillion, Marie de Serres, Richard Lapointe-Goupil, Isabelle Bairati, Pierre Gagnon, Michèle Deschamps, Josée Savard, François Meyer, Luc Bélanger, G Demers

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2006
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHôtel-Dieu de QuébecCentre hospitalier universitaire de QuébecMichel-SarrazinUniversité Laval
FundersCentre Hospitalier Universitaire de Québec
KeywordsPsychosocialOncology nursingMedicineOncologyNursingAdaptation (eye)Internal medicinePsychologyNurse education

Abstract

fetched live from OpenAlex

A profile of the role and functions of an oncology patient-navigator nurse (OPN) and the preliminary phases to implementing this role within a team specializing in oncology are first presented. This is followed by a qualitative study that provides a descriptive assessment for implementing an initial OPN in the head and neck oncology area of a university hospital centre (UHC) with a supraregional model for oncology. Three groups of stakeholders (individuals with cancer and families, caregivers, network partners) were interviewed on three occasions: before, during and after implementation. The results show that this new role can be integrated within a team specializing in oncology. The beneficial effects of this role on the process of adaptation to illness, interdisciplinary work and continuity of care are described. Several recommendations are formulated, one being the importance of situating the implementation process from an organizational change perspective.

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)0.005
metaresearch head score (Gemma)0.007
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.002
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.259
Teacher spread0.254 · 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

Citations73
Published2006
Admission routes3
Has abstractyes

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Same venueCanadian Oncology Nursing JournalSame topicChildhood Cancer Survivors' Quality of LifeFrench-language works237,207