Increasing Healthy Outcomes for Prostate Cancer Survivors : An Innovative Cooking Class Intervention Feasibility Study ; Study Summary, Archive of Course Curriculum, Recipes, and Photos
Bibliographic record
Abstract
Prostate cancer is the most commonly diagnosed cancer in Canadian men, with 1 in 7 men being diagnosed during their lifetime. Survival five years post-diagnosis in men aged 40-79 is more than 95%1; however, post-treatment, many survivors live with the side-effects of the disease and/or treatment and may develop chronic conditions due to aging and/or cancer therapy (e.g., bone loss, weight gain). Nutrition can play a role in preventing some of these side-effects or lessening their impact. Innovative approaches are urgently needed to prevent and mitigate the negative effects of prostate cancer and promote healthy lifestyles in prostate cancer survivors. Given that prostate cancer is a disease that affects not only the patient, but also family members, interventions that Includie both survivors and their partners hold particular promise of benefit. This study tested the feasibility of a novel behavioural approach: cooking classes for prostate cancer survivors and their spouses. The cooking class intervention consisted of six weekly two-hour group cooking classes, led by an educator/chef team. A total of 22 couples participated in the pilot study. All men in the study had been diagnosed with prostate cancer and were living with wife or common law partner. The program had successful accrual, retention, and adherence, with virtually no attrition or drop out. Evaluation data showed that 100% of participants reported that they found the class helpful, and they would recommend it to other men with prostate cancer and their partners. We also found promising outcome data, with analysis of anthropomorphic data showing a reduction in weight and waist and hip circumference for the majority of both survivors and spouses. These positive findings support the importance of replicating and extending this approach in larger populations, and exploring additional ways to provide the curriculum. We hope that such efforts can build on the well-received materials that we developed in this project. To that end, the course curriculum and the session-specific recipes are provided here and can be freely used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".