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Record W2112887727 · doi:10.5737/23688076254409414

An evaluation report of the nurse navigator services for the breast cancer support program

2015· article· en· W2112887727 on OpenAlexvenueno aff
Kris Trevillion, Savitri Singh‐Carlson, Frances Wong, Colleen Sherriff

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2015
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerMedicinePsychosocialNursingFamily medicineLikert scaleEmotional supportPsychosocial supportHealth carePatient satisfactionCancerSocial supportPsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this quality improvement project was to evaluate the effectiveness of breast cancer care support provided by breast cancer care navigators (BCCN) for women attending the breast health clinic (BHC). This evaluative process examined patients' satisfaction with the nurse navigator program that focused on addressing breast cancer patients' informational needs, emotional support, and guidance through the cancer trajectory. A survey approach using Likert-type scales and open-ended questions was utilized to gather data. Patients seen at the BHC between July 2011 and July 2013 were sent the surveys by mail. The 154 responses constituted a 69% response rate. More than 90% of participants understood the information provided by the BCCN and were satisfied with the information that had been received. Psychosocial support from patient/family counselling services at the agency and in the community were among the most common request for resources. Recommendations include contacting patients directly after their initial meeting at the clinic and at least once after their treatments began, to ensure continuity and support. BCCN role was identified as being valuable with a positive effect on patients' experience.

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)0.019
metaresearch head score (Gemma)0.024
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 score0.019
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.114
GPT teacher head0.549
Teacher spread0.436 · 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

Citations31
Published2015
Admission routes1
Has abstractyes

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