The Elusive Search for Success: Defining and Measuring Implementation Outcomes in a Real-World Hospital Trial
Bibliographic record
Abstract
Objective and study setting: Research efforts to identify factors that influence successful implementation are growing. This paper describes methods of defining and measuring outcomes of implementation success, using a cluster randomised controlled trial with 12 cancer services in Australia comparing the effectiveness of implementation strategies to support adherence to the Australian Clinical Pathway for the Screening, Assessment and Management of Anxiety and Depression in Adult Cancer Patients (ADAPT CP). Study design and methods: Using the StaRI guidelines, a process evaluation was planned to explore participant experience of the ADAPT CP, resources and implementation strategies according to the Implementation Outcomes Framework. This study focused on identifying measurable outcome criteria, prior to data collection for the trial, which is currently in progress. Principal findings: We translated each implementation outcome into clearly defined and measurable criteria, noting whether each addressed the ADAPT CP, resources or implementation strategies, or a combination of the three. A consensus process defined measures for the primary outcome (adherence) and secondary (implementation) outcomes; this process included literature review, discussion and clear measurement parameters. Based on our experience, we present an approach that could be used as a guide for other researchers and clinicians seeking to define success in their work. Conclusions: Defining and operationalising success in real-world implementation yields a range of methodological challenges and complexities that may be overcome by iterative review and engagement with end users. A clear understanding of how outcomes are defined and measured, based on a strong theoretical framework, is crucial to meaningful measurement and outcomes. The conceptual approach described in this article could be generalized for use in other studies.
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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 | 0.684 | 0.791 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".