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Record W4304587220 · doi:10.2196/40597

Barriers and Facilitators Influencing Real-time and Digital-Based Reporting of Adverse Drug Reactions by Community Pharmacists: Qualitative Study Using the Task-Technology Fit Framework

2022· article· en· W4304587220 on OpenAlexvenueno aff
Joel Fossouo Tagne, Reginald Amin Yakob, Rachael McDonald, Nilmini Wickramasinghe

Bibliographic record

VenueInteractive Journal of Medical Research · 2022
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacovigilance and Adverse Drug Reactions
Canadian institutionsnot available
FundersSwinburne University of Technology
KeywordsThematic analysisWorkflowPsychological interventionIncentiveQualitative researchAdverse drug reactionQualitative propertyMedicineTask (project management)Knowledge managementNursingDrugPharmacologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Medication use can result in adverse drug reactions (ADRs) that cause increased morbidity and health care consumption for patients and could potentially be fatal. Timely reporting of ADRs to regulators may contribute to patient safety by facilitating information gathering on drug safety data. Currently, little is known about how community pharmacists (CPs) monitor, handle, and report ADRs in Australia. OBJECTIVE: This study aimed to identify perceived barriers to and facilitators of ADR reporting by CPs in Australia and suggest digital interventions. METHODS: A qualitative study with individual interviews was conducted with CPs working across Victoria, Australia, between April 2022 and May 2022. A semistructured interview guide was used to identify perceived barriers to and facilitators of ADR reporting among CPs. The data were analyzed using thematic analysis. We constructed themes from the CP-reported barriers and facilitators. The themes were subsequently aligned with the Task-Technology Fit framework. RESULTS: A total of 12 CPs were interviewed. Identified barriers were lack of knowledge of both the ADR reporting process and ADR reporting systems, time constraints, lack of financial incentives, lack of organizational support for ADR reporting, inadequate IT systems, and preference to refer consumers to physicians. The proposed facilitators of ADR reporting included enhancing CPs knowledge and awareness of ADRs, financial incentives for ADR reporting, workflow-integrated ADR reporting technology systems, feedback provision to CPs on the reported ADRs, and promoting consumer ADR reporting. CONCLUSIONS: Barriers to and facilitators of ADR reporting spanned both the task and technology aspects of the Task-Technology Fit model. Addressing the identified barriers to ADR reporting and providing workplace technologies that support ADR reporting may improve ADR reporting by CPs. Further investigations to observe ADR handling and reporting within community pharmacies can enhance patient safety by increasing ADR reporting by CPs.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.009
Insufficient payload (model declined to judge)0.0010.000

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 head0.193
GPT teacher head0.579
Teacher spread0.386 · 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 teacher head, not a consensus.

Study designQualitative
Domainnot available
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

Citations11
Published2022
Admission routes1
Has abstractyes

Explore more

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