Quality care and patient involvement in their care: Preferences for transitioning cancer follow-up care to the community.
Bibliographic record
Abstract
75 Background: The cancer survivor rate is rising and a new focus has turned to the appropriate survivorship care for this new population. Although several models of survivorship care are currently present in Canada, few studies have examined cancer patient preferences. This study compared patient preferences for direct referral back to one’s primary care practitioner (PCP) after 2-3 years of oncology specialist care versustransition through a specialized transitional clinic before exclusive follow-up by one’s PCP. The main objectives were to: (1) assess patient interest in a specialized cancer transitional clinic model, and (2) compare patient and demographic characteristics by such preferences. Methods: A cross-sectional scenario-based survey of cancer survivors who had undergone curative therapy at the Princess Margaret Cancer Centre assessed patient preferences for the transitioning of their post-treatment cancer care. Regression models compared clinico-demographic and psychosocial variables (anxiety, depression, distress) to one’s preference for transition of care. Results: Among 242 cancer survivors, 54% were male, 78% Caucasian, 43% with a college degree, median household income between 60-75K, 71% married, with a wide distribution of curable cancer sites (anal, colorectal, breast, testicular, lymphoma, head/neck, lung). 77% preferred transition through a specialized transitional clinic versus direct referral back to their family doctor. No factors were found to be related to preference for transition of care, except individuals who scored high on anxiety using the ESAS scale were significantly more likely to prefer referral to the specialized transitional clinic (p<0.05). No differences in preference were found by other clinico-demographic factors. Conclusions: A specialized transitional clinic is a preferred survivorship option in three-quarters of cancer patients. Anxiety, but not other variables, was associated with preference for the specialized clinic. This study highlights the need for individual decision-making regarding survivorship options. This individualization of transitioning may help improve patient’s perception of quality cancer care.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".