Implementation of an electronic prospective surveillance model for cancer rehabilitation: a mixed methods study protocol
Bibliographic record
Abstract
INTRODUCTION: An electronic prospective surveillance model (ePSM) uses patient-reported outcomes to monitor impairments along the cancer pathway for timely management. Randomised controlled trials show that ePSMs can effectively manage cancer-related impairments. However, ePSMs are not routinely embedded into practice and evidence-based approaches to implement them are limited. As such, we developed and implemented an ePSM, called REACH, across four Canadian centres. The objective of this study is to evaluate the impact and quality of the implementation of REACH and explore implementation barriers and facilitators. METHODS AND ANALYSIS: We will conduct a 16-month formative evaluation, using a single-arm mixed methods design to routinely monitor key implementation outcomes, identify barriers and adapt the implementation plan as required. Adult (≥18 years) breast, colorectal, lymphoma or head and neck cancer survivors will be eligible to register for REACH. Enrolled patients complete brief assessments of impairments over the course of their treatment and up to 2 years post-treatment and are provided with a personalised library of self-management education, community programmes and when necessary, suggested referrals to rehabilitation services. A multifaceted implementation plan will be used to implement REACH within each clinical context. We will assess several implementation outcomes including reach, acceptability, feasibility, appropriateness, fidelity, cost and sustainability. Quantitative implementation data will be collected using system usage data and evaluation surveys completed by patient participants. Qualitative data will be collected through focus groups with patient participants and interviews with clinical leadership and management, and analysis will be guided by the Consolidated Framework for Implementation Research. ETHICS AND DISSEMINATION: Site-specific ethics approvals were obtained. The results from this study will be presented at academic conferences and published in peer-reviewed journals. Additionally, knowledge translation materials will be co-designed with patient partners and will be disseminated to diverse knowledge users with support from our national and community partners.
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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 | 0.127 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".