Impact of Health Information-Seeking Behavior and Personal Factors on Preferred Role in Treatment Decision Making in Men With Newly Diagnosed Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (PC) patients continue to have unmet information needs at the time of diagnosis and are often unable to communicate their preferences to physicians at the time of the treatment consultation. OBJECTIVE: The objective of the study was to determine the impact of health information-seeking behavior (HISB) and personal factors on patients' preferred role in treatment decision making (TDM). METHODS: Participants consisted of 150 men with newly diagnosed PC seen at 2 urology clinics in western Canada. A survey questionnaire was used to gather information on HISB, personal factors influencing treatment choice, and decision control. RESULTS: More than 90% of the participants reported a preference to play either an active or collaborative role in TDM and having either an "intense" or "complementary" HISB. No significant association was found between HISB and preferred role in TDM. Impact of treatment on survival and urinary function and the urologist's recommendation were identified as the 3 main factors influencing the treatment decision. CONCLUSIONS: At the time of diagnosis, the majority of men want to be involved in TDM and have access to information. Our findings suggest that the type and amount of information men want to access are dependent on HISB. Assessing factors having an impact on TDM may prove useful to guide patient-clinician treatment discussions. IMPLICATIONS FOR PRACTICE: This survey provides clinicians with a method to assess information and decision preferences of men with newly diagnosed PC and factors having an influence on treatment choice.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".