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Record W2398496128 · doi:10.5737/1181912x222129133

The Merck Lectureship: Communication: The key to improving the prostate cancer patient experience

2012· article· en· W2398496128 on OpenAlexaffvenueabout
Marian Waldie, Jennifer Smylie

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2012
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsProstate cancerMedicineCancerPatient educationInformation exchangeNursingMedical educationFamily medicineInternal medicineComputer science

Abstract

fetched live from OpenAlex

In 2010, an estimated 24,600 Canadian men were diagnosed with prostate cancer (Canadian Cancer Society, 2011). Upon diagnosis, men and their family members begin an arduous journey of information gathering surrounding prostate cancer and its various forms of treatment. Men have to consider the impact a treatment may potentially have on their quality of life and, frequently, they experience decisional conflict and require support. In May 2008, the Prostate Cancer Assessment Clinic opened to receive men for an evaluation of a possible prostate cancer. Our inter-professional model of care provides support, guidance and education to our patients from assessment to diagnosis and treatment planning. A major goal of our diagnostic assessment unit has been to improve the patient experience. Communication is defined as "to make known, to exchange information or opinions" (Cayne, Lechner, et al., 1988). Nursing is the critical link for information exchange that is patient-centred and collaborative. The focus of this paper will highlight the development and implementation of nurse-led initiatives within our program to improve the prostate cancer patient experience. These initiatives include: a patient information guide, prostate biopsy care, patient resources, community links, surgery education classes and implementation of a decision aid. Communication is the key.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0170.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.146
GPT teacher head0.447
Teacher spread0.302 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations7
Published2012
Admission routes3
Has abstractyes

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