Exploring Men’s Experiences with Follow-Up Care following Primary Treatment for Prostate Cancer in Atlantic Canada: A Qualitative Study
Bibliographic record
Abstract
Prostate cancer is a common and life-altering condition among Canadian men, yet little is known about how follow-up care is provided to those who have completed treatment. Despite improving survival rates, survivors experience ongoing needs and are often not provided with support to manage them. This study sought to investigate the post-treatment experiences and needs of prostate cancer survivors and to determine if and how these needs are being met. Using a qualitative description design, prostate cancer survivors who had completed treatment took part in semi-structured interviews. The interviews were recorded and analyzed thematically. The participants experienced varying levels of satisfaction with their follow-up care. While primary care providers played significant roles, continuity of care and specialist involvement varied. Most participants felt unprepared to manage the long-term effects of their cancer due to a lack of information and resources from their healthcare providers. Instead, participants turned to their peers for support. Ongoing physical and psychosocial needs went unmet and had significant impacts on their daily lives. Participants felt that support for these issues should be automatically integrated into their follow-up care. In summary, this study revealed the importance of integrated, patient-centered follow-up care for prostate cancer in Atlantic Canada.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".