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Record W2893521337 · doi:10.31729/jnma.863

PHYSICIAN-PATIENT COMMUNICATION REGARDING PRESCRIBED MEDICATION IN AN AMBULATORY CARE SETTING IN KATHMANDU, NEPAL

2003· article· en· W2893521337 on OpenAlexaboutno aff
Mohan P. Joshi, David A. Wachter, Keith Johnson, Bamdev Regmi, Rajendra Tamrakar, Suchitra Ranjit, Bir Bahadur Lama, R Sthapit, Sharmistha Dev, Rasish Subedi

Bibliographic record

VenueJournal of Nepal Medical Association · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMedical prescriptionRecallQuarter (Canadian coin)Family medicineDosingAmbulatoryPatient safetyNursingHealth careSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Physician-patient interactions often lead to prescription of medicines. Safety andcompliance in the use of these medicines are largely dependent on proper verbal aswell as written communication between prescriber and patient. However, severalpublished reports suggest that such communication is often inadequate. The presentstudy indicated suboptimal doctor-patient communication at a tertiary care hospitalin Nepal. Fifty-two (21.7%) of the 240 patients/caregivers interviewed after out-patientconsultation claimed that doctors did not provide any information on prescribedmedicines. Nearly a quarter of the 188 patients/caregivers who did report havingreceived information could not recall what they had been told, and in more than halfof these cases the lack of recall was attributed to problems in communication. Frequentuse of the English language and Latin abbreviation in prescribed dosing schedulesindicated a need for improvement in written communication as well.Key Words: prescribing information; communication; physician-patient interaction; Nepal.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.271
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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