Lessons in complexity learned during a Canadian virtual pragmatic trial for prostate cancer survivorship
Bibliographic record
Abstract
Background: Widespread adoption of digital health innovations (DHI) is often plagued with barriers to successful implementation. We share our experiences piloting and implementing our DHI, the Ned Clinic (Ned for “no evidence of disease”), to support prostate cancer survivors.Methods: We applied the non-adoption, abandonment, scale-up, spread and sustainability complexity assessment tool (NASSS-CAT) to outline implementation complexities at four cancer centres across Canada. We uncovered underlying factors that contribute to or explain implementation.Results: Factors identified included: service interruptions, costing structure, user experience (acceptability); patient referral, overscheduling, incentivization, timing (appropriateness); competing institutional changes, misaligned value proposition, and requisite digital literacy (adoption). Solutions and changes made that carried into the trial included agile development, shifting responsibilities (e.g. tech support, clinic-personalized recruitment strategies), increasing in-person socialization of Ned Clinics after COVID-19 lockdowns, and enhanced documentation.Discussion: Implementing new and complex interventions in a complex adaptive system requires an element of trial and error to find what works best. Adaptations between the pilot and trial can compensate for complexity. Ongoing multidisciplinary stakeholder engagement was crucial for project success especially as complexities arose.Conclusion: Our approach has informed how agile adaptations can improve target implementation outcomes and may be transferable to other DHI contexts.
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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 | 0.242 | 0.320 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".