The Merck Lectureship: Communication: The key to improving the prostate cancer patient experience
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.060 | 0.016 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".