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Record W2509241676 · doi:10.1097/jhq.0000000000000055

Medication Knowledge Among Older Adults Admitted to Home Care in Ontario During 2012–2013

2016· article· en· W2509241676 on OpenAlexaboutno aff
Kim Sears, Kevin Woo, Joan Almost, Rosemary Wilson, Eliot Frymire, Marlo Whitehead, Elizabeth G. VanDenKerkhof

Bibliographic record

VenueJournal for Healthcare Quality · 2016
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsGerontologyMedicineFamily medicinePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Medication use among Canadian seniors is widespread and increases with the number of comorbidities. Limited evidence exists on medication knowledge among seniors, especially in home care. PURPOSE: The purpose of this retrospective cohort study was to describe medication knowledge and ability to take medication among seniors admitted to home care in Ontario. RESULTS: Ten percent had little or no knowledge of what medication to take (n = 1,389/14,004) or an understanding of the purpose of their medications (n = 1,396/14,004). Increasing numbers of medications prescribed was associated with decreased knowledge of medications. The strongest predictor of limited knowledge and ability to take medication was dementia (odds ratio > 5.0). DISCUSSION: Among Ontario seniors living at home, knowledge about medications decreases as the number of medications increases. Therefore, this group may be at high risk of medication errors. CONCLUSION: Better systems are required to allow healthcare professionals to review with patients, any medications with patients and caregivers, to assist in addressing the decreased knowledge of medications. Such a system would provide the capacity to target those individuals at high risk for a medication error, as well as the medications and drug-drug interactions that seem most likely to be harmful among older adults.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.145
GPT teacher head0.477
Teacher spread0.333 · 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.

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

Citations7
Published2016
Admission routes1
Has abstractyes

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