Improving audit and feedback: A user-centred approach to designing feedback techniques for an online experiment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: Audit and feedback (A&F) programmes aim to improve patient care by providing summary data on performance to clinicians. They generally have modest, but variable, effects on patient care and questions remain about how best to provide performance feedback. It is not feasible to test all ways of providing feedback in ‘real-world’ randomised trials. Online screening experiments that screen feedback techniques prior to real-world evaluations of optimised versions offer a systematic approach. User-centred design methodologies can inform the design of such online experiments. Methods: We report the use of an innovative user-centred design approach to create feedback techniques for an online screening experiment and reflect on its usefulness. This approach included the involvement of patients and stakeholders. Results and Conclusion: We highlight lessons on ways to engage with partners, considering the feasibility of online A&F feedback delivery, fidelity, and usability. We demonstrate how the approach was implemented to co-create a set of feedback techniques for an online experiment and could also be applied to the design of other digital interventions.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it