Proceedings of the 5th UK Implementation Science Research Conference
Bibliographic record
Abstract
BackgroundGERONTE is an EU funded project designed to improve the quality of life for older cancer patients with comorbidity by designing, implementing, and testing a novel technology-supported care pathway.Achieving efficiency and personalised care requires complex change to healthcare systems.Information Technology can support needed coordination (data sharing, communication, safety checks) on a large and sustainable scale.Implementing change into existing systems has high failure rates, due to patient and organisational-related complexity, highlighting the need for tailored, agile implementation plans.Implementing Science has established core theories and frameworks, but limited evidence on frameworks for complex interventions using technology. MethodThe aim is to co-create a framework to support widespread sustained implementation of the GERONTE intervention by identifying the: 1) intervention's mechanism of action; and the 2) contexts and strategies that impact implementation.An Action Research approach, using analysis and synthesis of qualitative and quantitative data, collected from the literature, and interviews, observation, and surveys with stakeholders, to co-design, test and refine the framework. ResultsThe framework is at the co-creation stage, with analysis across stakeholders and contexts, to identify key factors that impact GERONTE's design, adaption, and implementation.The CLO-uT framework will build on, and apply, existing Implementation Science knowledge to support the implementation of innovative solution in line with changing healthcare needs and technological developments.Conclusion CIO-uT will provide a practical user-friendly framework to support the implementation of complex technology-supported interventions GerOnTe: Streamlined Geriatric and Oncological evaluation based on IC Technology for holistic patient-oriented healthcare management for older multimorbid patients.
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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 | 0.148 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.148 | 0.026 |
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".