Factors affecting patient reporting of adverse drug reactions: a systematic review
Bibliographic record
Abstract
AIM: The aim of the present study was to determine the barriers and motives influencing consumer reporting of adverse drug reactions (ADRs). METHODS: A systematic review, guided by the Cochrane Handbook, was conducted. Electronic searches included MEDLINE, EMBASE, PsycINFO, CINAHL, PubMed and the Cochrane Database of Systematic Reviews from 1964 to December 2014. Eligible studies addressed patients' perceptions and factors influencing ADR reporting. Studies about healthcare professional (HCP) reporting of ADRs were excluded. Studies were appraised for quality, and results were analysed descriptively. RESULTS: Of 1435 citations identified, 21 studies were eligible. Studies were primarily conducted in the UK, the Netherlands and Australia. The identified barriers to patient reporting of ADRs (n = 15 studies) included poor awareness, confusion about who should report the ADR, difficulties with reporting procedures, lack of feedback on submitted reports, mailing costs, ADRs resolved and prior negative reporting experiences. The identified motives for patients reporting ADRs (n = 10 studies) were: preventing others from having similar ADRs, wanting personal feedback, improving medication safety, informing regulatory agencies, improving HCP practices, responding to HCPs not reporting their ADRs and having been asked to report ADRs by HCPs. CONCLUSIONS: Most patients were not aware of reporting systems and others were confused about reporting. Patients were mainly motivated to make their ADRs known to prevent similar suffering in other patients. By increasing patient familiarity and providing clear reporting processes, reporting systems could better achieve patient reporting of ADRs.
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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 | 0.014 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".