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Record W2944755240 · doi:10.1177/2333721419845179

Demonstrating a Technology-Mediated Intervention to Support Medication Adherence in Community-Dwelling Older Adults in Primary Care: A Feasibility Study

2019· article· en· W2944755240 on OpenAlexafffundabout
Henry Siu, Bethany Delleman, Jessica Langevin, Dee Mangin, Michelle Howard, Qiyin Fang, David Price, David W. Chan

Bibliographic record

VenueGerontology and Geriatric Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsMcMaster University
FundersCanadian Frailty Network
KeywordsMedicineIntervention (counseling)Primary carePharmacistFamily medicinePhysical therapyGerontologyNursingPharmacy

Abstract

fetched live from OpenAlex

Background: Medication non-adherence can lead to significant morbidity and mortality. This 4-week feasibility study aims to demonstrate that the eDosette intervention can be implemented with older adults in primary care. Method: Fifty-six older adults from four primary care sites in Southwestern Ontario, Canada participated. The intervention involved generating, for pharmacist review, weekly medication administration records based on transmitted data captured by the eDosette. The primary outcome is implementation feasibility defined by recruitment, adherence rates, frequency of captured missed and late doses, descriptions of clinical work resulting from the intervention, and participant feedback. Results: The recruitment rate was 24% (57/240); one withdrew due to personal reasons. The mean observed adherence rate was 82% (range 49%-100%). Overall, participants missed 505 and took 2,105 doses late; 118 clinical decisions occurred with 72 unique medication changes in 31 participants. Participants found the eDosette easy to use and did not feel that they were viewed negatively because of their potential non-adherence. Conclusion: The eDosette intervention could be feasibly implemented in primary care with older adults. Providing information about when an older adult takes their medications could play a role in medication adherence by prompting more informed discussions between the older adult and primary care clinicians.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.337
Teacher spread0.301 · 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

Citations4
Published2019
Admission routes3
Has abstractyes

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