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Record W2076151388 · doi:10.1191/1078155203jp104oa

Implementation of a telephone callback service for ambulatory oncology patients

2003· article· en· W2076151388 on OpenAlexaff
Roxanne Dobish, Karen Tulloch, Carole Chambers

Bibliographic record

VenueJournal of Oncology Pharmacy Practice · 2003
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of AlbertaAlberta Cancer Foundation
FundersMedical Research Council
KeywordsCallbackMedicinePharmacyAmbulatoryPharmacistWorkloadService (business)Family medicineMedical emergencySurgery

Abstract

fetched live from OpenAlex

Introduction. A pilot project was established at the Cross Cancer Institute pharmacy to assess the feasibility of implementing a patient callback program to determine which patients would benefit from a callback and the impact a callback service would have on the workload of the pharmacy department. Program description/development/implementation/evaluation. A pharmacy student conducted the pilot project over a 14-week time period. Four categories of patients were selected for inclusion in the pilot. The student approached patients at the time of medication pick-up to receive verbal permission to be included in the callback program. A standardized callback form was utilized to record medication information and any recommendations made. Data were collected on the numbers of patients in each category with questions or concerns as this was felt to be indicative of patients who would most benefit from a callback. The total time spent making the telephone calls was recorded. Over the eight-week period, 530.5 minutes were spent making the telephone calls with the average length of call being 3.27 minutes. Discussion/conclusion. The student researcher proposed that three patient groups would benefit from receiving a callback: new patients or patients on new medications; elderly patients; and patients with specific disease-drug combinations. The project also provided an estimate of pharmacist time required to implement such a service and it was concluded that implementation could occur with minimal disruption to workflow.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.168
GPT teacher head0.536
Teacher spread0.368 · 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 designNot applicable
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

Citations5
Published2003
Admission routes1
Has abstractyes

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