Exploring the influencing factors of adverse drug reaction reporting among medical personnel: a COM-B model-based study
Bibliographic record
Abstract
Background: This study aims to identify the factors that influence medical workers' enthusiasm for reporting adverse drug reactions (ADRs). Understanding these factors is essential to implement targeted interventions that can improve and refine pharmacovigilance systems. Methods: We adopted the Capability, Opportunity, Motivation, and Behavior model (COM-B) model as the theoretical framework and conducted qualitative research using in-depth interviews with clinicians, nurses, pharmacists, and administrators. 24 one-on-one interviews were conducted and audio-recorded. The interviews were transcribed verbatim, and subjected to thematic analysis to uncover the key factors affecting ADR reporting among medical staff. Results: The participation included 24 healthcare workers from six different healthcare organisations. Analysis revealed that decreased motivation to report ADRs was due to inadequate judgment or inconsistent judgment criteria within the capability domain, poor awareness of ADRs and deficient communication skills within the psychological domain, unclear responsibilities within the motivation domain, and limited or no access to necessary resources within the opportunity domain. Facilitators of ADR reporting included sufficient cognitive and operational abilities, spontaneous and incentivized motivation, clear responsibilities and role expectations, and robust social support. Conclusion: There is a critical need to develop comprehensive interventions that address the identified factors influencing ADR reporting. By improving the motivation of medical staff to report ADRs, the pharmacovigilance system can be significantly improved.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".