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Record W2905187315 · doi:10.9778/cmajo.20180090

Quality-related events reported by community pharmacies in Nova Scotia over a 7-year period: a descriptive analysis

2018· article· en· W2905187315 on OpenAlexaffvenueabout
Adrian Boucher, Certina Ho, Neil J. MacKinnon, Todd A. Boyle, Andrea Bishop, Paola A. González, Christopher M. Hartt, James R. Barker

Bibliographic record

VenueCMAJ Open · 2018
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of TorontoIzaak Walton Killam Health CentreSt. Francis Xavier UniversityDalhousie University
Fundersnot available
KeywordsNova scotiaPharmacyDescriptive statisticsMedical prescriptionMedicineQuality (philosophy)Family medicineQuality managementPatient safetyEmergency medicineMedical emergencyNursingHealth careBusinessGeographyStatisticsService (business)

Abstract

fetched live from OpenAlex

BACKGROUND: Quality-related events are defined as medication errors that reach the patient (e.g., incorrect drug, dose and quantity), in addition to medication errors that are intercepted before dispensing (i.e., near misses). The aim of this study is to quantify and characterize such events as reported by community pharmacies in a Canadian province. METHODS: A retrospective analysis was conducted on quality-related events reported to the Community Pharmacy Incident Reporting system from 301 community pharmacies in Nova Scotia between Oct. 1, 2010, and June 30, 2017. We performed a descriptive analysis on these events with respect to the discoverer, patient outcome, medication system stages and type. RESULTS: = 928) were associated with patient harm. Incorrect dose or frequency, incorrect quantity and incorrect drug were the most common types of quality-related events reported. Most of the quality-related events occurred at order entry, followed by preparation and dispensing, and prescribing. INTERPRETATION: Quality-related events reported by community pharmacies differ from those reported in institutional settings with respect to patient outcome, medication system stages and type. This analysis provides valuable information to guide quality improvement initiatives to strengthen medication safety in community pharmacies.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.294
GPT teacher head0.500
Teacher spread0.206 · 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 designObservational
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

Citations21
Published2018
Admission routes3
Has abstractyes

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