Evaluating an educational intervention to alleviate distress amongst men with newly diagnosed prostate cancer and their partners
Bibliographic record
Abstract
OBJECTIVE: To determine whether an education session alleviates distress for both patients with prostate cancer and their partners; and whether their partner's attendance at the session; and disease, treatment, and sociodemographic characteristics affect changes in distress levels. PATIENTS, SUBJECTS AND METHODS: We identified men with untreated prostate cancer at the Vancouver Prostate Centre between February 2015 and March 2016 who agreed to attend our education session. The session consisted of a didactic presentation covering the biology of prostate cancer, treatment options, and side-effects, followed by a private joint session with a urologist and radiation oncologist. We assessed distress using the Distress Thermometer (DT) and compared pre- and post-session distress, and change in distress between patients and partners using matched and unmatched t-tests, respectively. We also assessed pre-session anxiety using the seven-item Generalised Anxiety Disorder measure, and decisional certainty using the Decisional Conflict Scale. RESULTS: In all, 71 patients and 48 partners participated in the study. Attending the session led to a significant reduction in the median DT score for patients (4.0-3.0, P < 0.01) and partners (5.0-4.0, P = 0.02). Partners reported higher distress both before and after the session (4.9 vs 3.8, P = 0.03 pre-session and 4.2 vs 3.2, P = 0.03 post-session). The presence of a partner at the session did not affect patients' pre- or post-session distress or the success of the session at alleviating distress. Sociodemographic and clinical characteristics had little effect on distress levels. CONCLUSIONS: An interdisciplinary education session is equally effective at alleviating distress for both patients with prostate cancer and their female 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".