Designing A Provincial Surveillance and Support System for Childhood Cancer Survivors
Bibliographic record
Abstract
Abstract Background Survivors of childhood cancer are at lifelong risk of morbidity (such as new cancers or heart failure) and premature mortality because of their cancer treatment. These are termed late effects. Therefore, they require lifelong, risk-tailored surveillance. However, most adult survivors of childhood cancer do not complete recommended surveillance tests such as mammograms or echocardiograms. Working with survivors, family physicians, and health-system partners, we are designing a provincial support system for high-priority tests, informed by principles of implementation science, behavioral science and design-thinking. Methods Our multi-phase process was as follows: Step 1: a qualitative study to explore intervention components essential to accessing surveillance tests; Step 2: a workshop with childhood cancer survivors, family physicians and health system stakeholders that used the Step 1 findings and ‘personas’ (a series of fictional but data-informed characters) to develop and tailor the intervention for different survivor groups Step 3: intervention prototype development; and step 4: iterative user-testing. Results The qualitative study of 30 survivors and 7 family physicians found a high desire for information on surveillance for late effects. Respondents indicated that in addition to providing personalized information, the intervention should help patients book appointments when they are due. Insights from the workshop included the importance of partnering with both family physicians and survivorship clinics and providing emotional support for survivors that may experience distress upon learning of their risk for late effects. In our user-testing process, prototypes went through iterations that incorporated feedback from users regarding usability and functionality. We sought to address the needs of survivors and physicians while balancing the capacity and infrastructure available for a life-long intervention via our health system partners. Conclusion In partnership with childhood cancer survivors, family physicians, and health-system partners, we elucidated the barriers and enablers to accessing guideline-recommended surveillance tests and designed a multi-faceted solution that will support survivors and their family physician. The next step is to evaluate the intervention in a pragmatic randomized control trial.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".