Testing E-mail Content to Encourage Physicians to Access an Audit and Feedback Tool: A Factorial Randomized Experiment
Bibliographic record
Abstract
Background: In Ontario, an online audit and feedback tool that provides primary care physicians with detailed information about patients who are overdue for cancer screening is underused. In the present study, we aimed to examine the effect of messages operationalizing 3 behaviour change techniques on access to the audit and feedback tool and on cancer screening rates. Methods: During May–September 2017, a pragmatic 2×2×2 factorial experiment tested 3 behaviour change techniques: anticipated regret, material incentive, and problem-solving. Outcomes were assessed using routinely collected administrative data. A qualitative process evaluation explored how and why the e-mail messages did or did not support Screening Activity Report access. Results: Of 5449 primary care physicians randomly allocated to 1 of 8 e-mail messages, fewer than half opened the messages and fewer than 1 in 10 clicked through the messages. Messages with problem-solving content were associated with a 12.9% relative reduction in access to the tool (risk ratio: 0.871; 95% confidence interval: 0.791 to 0.958; p = 0.005), but a 0.3% increase in cervical cancer screening (rate ratio: 1.003; 95% confidence interval: 1.001 to 1.006; p = 0.003). If true, that association would represent 7568 more patients being screened. No other significant effects were observed. Conclusions: For audit and feedback to work, recipients must engage with the data; for e-mail messages to prompt activity, recipients must open and review the message content. This large factorial experiment demonstrated that small changes in the content of such e-mail messages might influence clinical behaviour. Future research should focus on strategies to make cancer screening more user-centred.
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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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".