Developing a Provincial Surveillance and Support System for Childhood Cancer Survivors: Multiphase User-Centered Design Study
Bibliographic record
Abstract
BACKGROUND: Survivors of childhood cancer are at lifelong risk of morbidity (such as new cancers or heart failure) and premature mortality due to 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. OBJECTIVE: In partnership 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 multiphase process was structured as follows. Step 1 consisted of a qualitative study to explore intervention components essential to accessing surveillance tests. Step 2 comprised 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 consisted of intervention prototype development, and Step 4 involved 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 the intervention should help patients book appointments when they are due in addition to providing personalized information. Insights from the workshop included the importance of partnering with both family physicians and survivorship clinics and providing emotional support for survivors who 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 acceptability, usability, and functionality. We sought to address the needs of survivors and physicians while balancing the capacity and infrastructure available for a lifelong intervention via our health system partners. CONCLUSIONS: 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 multifaceted solution that will support survivors and their family physicians. The next step is to evaluate the intervention in a pragmatic randomized controlled trial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".