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Record W2892503424 · doi:10.2147/iprp.s148266

Current perspectives on pharmacist home visits: do we keep reinventing the wheel?

2018· review· en· W2892503424 on OpenAlexaff
Priti S. Flanagan, Andrea Barns

Bibliographic record

VenueIntegrated Pharmacy Research and Practice · 2018
Typereview
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsFraser HealthUniversity of British Columbia
Fundersnot available
KeywordsPharmacistMedicinePharmacyNursingClinical pharmacyPsychological interventionMedication therapy managementHealth careFamily medicine

Abstract

fetched live from OpenAlex

The scope of clinical pharmacy services available in outpatient settings, including home care, continues to expand. This review sought to identify the evidence to support pharmacist provision of clinical pharmacy services in a home care setting. Seventy-five reports were identified in the literature that provided evaluation and description of clinical pharmacy home visit services available around the world. Based on results from randomized controlled trials, pharmacist home visit interventions can improve patient medication adherence and knowledge, but have little impact on health care resource utilization. Other literature reported benefits of a pharmacist home visit service such as patient satisfaction, improved medication appropriateness, increased persistence with warfarin therapy, and increased medication discrepancy resolution. Current perspectives to consider in establishing or evaluating clinical pharmacy services offered in a home care setting include: staff competency, ideal target patient population, staff safety, use of technology, collaborative relationships with other health care providers, activities performed during a home visit, and pharmacist autonomy.

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)0.007
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.005
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0020.001
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.593
GPT teacher head0.622
Teacher spread0.029 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations29
Published2018
Admission routes1
Has abstractyes

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