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Record W7139420718

Barriers to the reporting of adverse drug reactions

2009· other· en· W7139420718 on OpenAlexaboutno aff
Linda N. Peterson, Robert Peterson, Kendall Ho, Tunde Olatunbosun

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsDrug reactionHealth professionalsDescriptive statisticsHealth careMEDLINECertaintyCompliance (psychology)Data collection
DOInot available

Abstract

fetched live from OpenAlex

Background: Voluntary reporting of Adverse Drug Reactions (ADRs) by physicians and other healthcare professionals currently is the single most important source of information for early signal detection of ADRs. Despite the importance of ADR reporting, there is evidence that less than 10% of ADRs experienced by Canadians are reported to Health Canada and the factors contributing to this under-reporting are unclear. Objectives: The purpose of the present study was to ascertain the barriers to reporting ADRs, including knowledge; attitudes and beliefs; and the accessibility of the reporting system as the first step in understanding the problem of underreporting of ADRs. In addition, information was collected about physician engagement in a dialogue about ADRs with their patients, and whether their office practice was structured to allow patient follow-up of an ADR. Methods: An online survey was administered to BC physicians in the spring of 2008. The survey was designed to obtain information in the following areas: knowledge of and attitudes toward ADRs; patient dialogue about ADRs and structure of the clinical practice; access to the report form, knowledge about if and what needs to be reported and educational support required for ADR reporting and appropriate utilization of new medications. Results: Eighty-seven physicians completed the survey, a response rate of 2%. There is a 95% level of certainty that the quantitative results provided in this survey are within a sampling margin of error of plus or minus 10.5%. Descriptive statistics were calculated and responses to some questions were analyzed using cross-tabulation by whether the physician had ever reported an ADR. A difference of p<0.05 was considered significant. The majority viewed the reporting of ADRs as a professional responsibility, discussed ADRs with their patients and structured their office practice to permit follow-up with suspected ADRs. However, 70% percent had never reported an ADR. Gaps in knowledge about adverse drug reactions and the subsequent reporting process were revealed. Participants indicated the need for decision-making support at the time an ADR is suspected and feedback on the reports they submit. Conclusion: The responses of this small and highly motivated subset of physicians, indicate that difficulties in the actual mechanism of reporting ADRs, rather than negative physician attitudes as suggested in the literature, may be an important first barrier to reporting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.097
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.009
GPT teacher head0.197
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainReporting
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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