Provision of Individualized Information to Men and Their Partners to Facilitate Treatment Decision Making in Prostate Cancer
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To determine if providing individualized information to men who are newly diagnosed with prostate cancer and their partners would lower their levels of psychological distress and enable them to become more active participants in treatment decision making. DESIGN: Quasiexperimental, one group, pretest/post-test. SETTING: The Prostate Centre at Vancouver General Hospital in British Columbia, Canada. SAMPLE: Convenience sample of 74 couples. 73 men had early-stage prostate cancer. Mean age of the men was 62.2 years, and mean age of the partners was 58.1 years. The majority (> 50%) had received their high school diplomas. METHODS: Respondents completed measures of decision preferences and psychological distress at the time of diagnosis and four months later. All participants used a computer to identify their information and decision preferences. Computer-generated, graphic printouts were used to guide the information counseling session. FINDINGS: Patients reported assuming a more active role in medical decision making than originally intended, partners assumed a more passive role in decision making than originally intended, and all participants had lower levels of psychological distress at four months. CONCLUSIONS: Evidence supports the need to provide informational support to couples at the prostate cancer diagnosis to facilitate treatment decision making and lower levels of psychological distress. Future research is needed to evaluate this type of approach in the context of a randomized clinical trial design. IMPLICATIONS FOR NURSING: The personalized, computer-graphic printouts can provide clinicians with an innovative method of guiding information counseling and providing decisional support to men with prostate cancer and their partners.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".