Patient educational technologies and their use by patients diagnosed with localized prostate cancer
Bibliographic record
Abstract
BACKGROUND: Two urology practices in Calgary, Canada use patient educational technology (PET) as a core component of their clinical practice. The purpose of this study was to determine how patients interact with PET designed to inform them about their treatment options for clinically localized prostate cancer. METHODS: A PET library was developed with 15 unique prostate-related educational modules relating to diagnosis, treatment options, and potential side effects. The PET collected data regarding its use, and those data were used to conduct a retrospective analysis. Descriptive analyses were conducted and comparisons made between patients' utilization of the PET library during first and subsequent access; Pearson's Chi-Square was used to test for statistical significance, where appropriate. RESULTS: Every patient (n = 394) diagnosed with localized prostate cancer was given access to the PET library using a unique identifier. Of those, 123 logged into the library and viewed at least one module and 94 patients logged into the library more than once. The average patient initially viewed modules pertaining to their diagnosis. Viewing behavior significantly changed in subsequent logins, moving towards modules pertaining to treatment options, decision making, and post-surgical information. DISCUSSION: As observed through the longitudinal utilization of the PET library, information technology offers clinicians an opportunity to provide an interactive platform to meet patients' dynamic educational needs. Understanding these needs will help inform the development of more useful PETs. CONCLUSION: The informational needs of patients diagnosed with clinically localized prostate cancer changed throughout the course of their diagnosis and treatment.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".