Accompanying People Affected by Cancer in Their Return to Life After Treatment: A Report on an Experiment Conducted in Canada
Bibliographic record
Abstract
This study aims to assess family doctors' perceived needs for improved patient follow-up post-acute treatment in oncology departments, specifically focusing on the Patient Oriented Discharge Summary (PODS) for individuals living with cancer. A cross-sectional quantitative survey targeted family doctors, and a before/after exploratory study was conducted with patients to measure their needs pre- and post-PODS implementation. Twenty-one out of 42 family doctors participated in the survey (50%). Patient data was collected at three points in time: prior to PODS implementation (T1, n = 20/30; 77%), one month later (T2, n = 20/26; 77%), and six months later (T3, n = 21/28; 75%). Descriptive statistics were used for all inquiries. Results revealed that 52.24% of family doctors lacked information from oncology teams about patient treatments and their progress, while 90.48% received no guidance on monitoring patients for symptoms or necessary tests once treatment was completed, despite everyone expressing the desire to perform such monitoring. Family doctors recommended using standardized sheets with patient information (47%), details of side effects (41%), and post-treatment follow-up plans (12%). At T1, 60% of patients received the necessary information, at T2 95% and 81% at T3. Regarding instructions provided to family caregivers, satisfaction levels were 40% at T1, 90% at T2, and 62% at T3. The study underscores the imperative of enhancing communication between oncology specialists and family physicians, facilitating the latter's follow-up of patients completing acute treatment. It also highlights the need for patients to be adequately prepared for the transition through effective use and sustained use of PODS.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".