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Record W2787231824 · doi:10.1177/1084822318754844

Safety Outcomes With Home Assessment Trial: A Mixed-Methods Evaluation of Medication Safety in the Home Care Setting

2018· article· en· W2787231824 on OpenAlexaff
Priti S. Flanagan, Adriana Briseño‐Garzón, Robert M. Strain

Bibliographic record

VenueHome Health Care Management & Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsFraser HealthUniversity of British Columbia
Fundersnot available
KeywordsMedicinePharmacistPatient safetyAdverse effectMEDLINEMedication therapy managementFamily medicineMedical emergencyNursingEmergency medicineHealth carePharmacy

Abstract

fetched live from OpenAlex

Adverse drug events (ADEs), a subset of AEs (adverse events), occur among home care recipients, yet the topic of medication safety in this setting is understudied and under reported. The Safety Outcomes With Home Assessment Trial (SO WHAT) was a mixed-methods, pilot study that sought to evaluate the feasibility of prospective evaluation and inform future research in the home care setting to better understand medication use and safety. Over 21 months, 19 patients were recruited. Feasibility of the study was improved by focused recruitment efforts and connection with primary care. All participants had medication issues identified by the pharmacist’s medication review and almost half had a suspected ADE. Patient experience themes identified related to knowledge about medications, communication, and home care human resources.

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)0.027
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.088
GPT teacher head0.527
Teacher spread0.440 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2018
Admission routes1
Has abstractyes

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