Cancer follow-up care in New Brunswick: cancer surveillance, support issues and fear of recurrence.
Bibliographic record
Abstract
The purpose of this study was to find out, from the patient's perspective and using qualitative methodology, how cancer follow-up care is managed in a New Brunswick health region. From focus group discussions with 23 participants 1-year post-cancer diagnosis, 3 prominent themes emerged: fear of recurrence, cancer surveillance/testing and support issues. The fear of recurrence permeates day-to-day life for many patients. To allay these fears, some patients feel a need to be subjected to extensive cancer surveillance. Emotional support, which is important for survivors, is complex. The majority of the participants in this study received cancer follow-up care from specialists. More rural than urban participants received their follow-up care from their family physicians (FPs). Participants had high expectations for follow-up care, regardless of which type of physician--specialist or FP--provided it. If physicians did not provide the level and intensity of care expected by their patients, they were considered uncaring. We advocate a "transition of care" or "shared care" protocol between the acute cancer treatment provider and the FP, particularly in rural areas. This would ensure that cancer patients have a clear understanding of where to turn for ongoing surveillance, when they fear cancer recurrence or need support. For optimized cancer follow-up care, physicians must be cognizant that careful emotional and clinical management over an indefinite period of time is required, and they must recognize the individual needs of each patient.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".