Comparing the application of two theoretical frameworks to describe determinants of adverse medical device event reporting: secondary analysis of qualitative interview data
Bibliographic record
Abstract
BACKGROUND: Post-market surveillance of medical devices is reliant on physician reporting of adverse medical device events (AMDEs). Few studies have examined factors that influence whether and how physicians report AMDEs, an essential step in the development of behaviour change interventions. This study was a secondary analysis comparing application of the Theoretical Domains Framework (TDF) and the Tailored Implementation for Chronic Diseases (TICD) framework to identify potential behaviour change interventions that correspond to determinants of AMDE reporting. METHODS: A previous study involving qualitative interviews with Canadian physicians that implant medical devices identified themes reflecting AMDE reporting determinants. In this secondary analysis, themes that emerged from the primary analysis were independently mapped to the TDF and TICD. Determinants and corresponding intervention options arising from both frameworks (and both mappers) were compared. RESULTS: Both theoretical frameworks were useful for identifying interventions corresponding to behavioural determinants of AMDE reporting. Information or education strategies that provide evidence about AMDEs, and audit and feedback of AMDE data were identified as interventions to target the theme of physician beliefs; improving information systems, and reminder cues, prompts and awards were identified as interventions to address determinants arising from the organization or systems themes; and modifying financial/non-financial incentives and sharing data on outcomes associated with AMDEs were identified as interventions to target device market themes. Numerous operational challenges were encountered in the application of both frameworks including a lack of clarity about how directly relevant to themes the domains/determinants should be, how many domains/determinants to select, if and how to resolve discrepancies across multiple mappers, and how to choose interventions from among the large number associated with selected domains/determinants. CONCLUSIONS: Given discrepancies in mapping themes to determinants/domains and the resulting interventions offered by the two frameworks, uncertainty remains about how to choose interventions that best match behavioural determinants in a given context. Further research is needed to provide more nuanced guidance on the application of TDF and TICD for a broader audience, which is likely to increase the utility and uptake of these frameworks in practice.
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How this classification was reachedexpand
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 | 0.024 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".