Assessing self‐help issues for patients with prostate cancer, interstitial cystitis, erectile dysfunction and urinary diversion
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of self-help (which is widely available and can assist clinicians to educate and empower patients) for several urological diseases. PATIENTS AND METHODS: Using a structured interview, the prevalence of self-help use and awareness was assessed in patients with prostate cancer, interstitial cystitis, erectile dysfunction and urinary diversion. Patients were also asked to report levels of support and information they required and to specify how well these needs were being met. Consecutive patients (120, 30 from each group) were recruited from an outpatient urology clinic in a tertiary-care health centre. RESULTS: Patients with interstitial cystitis and erectile dysfunction reported having the greatest need but were least satisfied with the level of support and information they currently receive. Inversely, patients with prostate cancer reported having the least need and were most satisfied. Excluding brochures, the use and awareness of self-help were low in all groups but most patients claimed they would use many types of self-help if they were available. Many patients are not directed to self-help resources by their urologist or family physician. CONCLUSION: Most patients would like to use more self-help but many do not. The low percentage of patients referred to self-help by their physician probably contributes to, or causes, this problem. It is important for clinicians to encourage and refer patients to specific resources to ensure that the patient receives accurate and pertinent information. As most patients read brochures it is logical to use them to provide basic information and to direct them to other more sophisticated forms of self-help. We have used urology patients as a model but consider that many of our findings could be applied to other medical and surgical specialties.
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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 | 0.006 |
| 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) | 0.002 | 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".