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Record W3049748640 · doi:10.19166/med.v7i6.2596

Assessment of Doctor-Patient Communication Among Residents in Internal Medicine Polyclinic At RSUP Dr. Mohammad Hoesin Palembang 2014 Using Simplified Checklist of Calgary Cambridge Guide

2020· article· en· W3049748640 on OpenAlexaboutno aff
Dipika Awinda, Rizma Adlia Syakurah, Mariatul Fadilah

Bibliographic record

VenueMedicinus · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMental Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsPolyclinicChecklistMedicineFamily medicineMedical recordPsychologyInternal medicine

Abstract

fetched live from OpenAlex

<p><strong>Introduction</strong><strong> </strong><strong>: </strong>Misperceptions between doctors and patients can bring negative impact for both the doctors and patients. Misperceptions may occur due to miscommunication during doctor-patient communication. Therefore, assessment during the communication process is necessary.</p><p><strong>Methods</strong><strong> :</strong> This study was a descpritive study with qualitative approach. <em>Checklist Calgary Cambridge Guide</em> (CCCG) was chosen as instrument because it has been widely used in many country.The study was condicted in Polyclinic Internal Medicine of dr. Mohammad Hoesin Hospital due to its high patient load with various diseases that is suitable for doctor-patient communication observation. Subjects were six residents in the department. Observation was done during the communication process. Deep interview was then done to assess the resident’s knowledge and opinions in doctor-patient communication and barriers related to it. </p><p><strong>Results : </strong>Majority of the residents failed to do some points of the CCCG, which includes self introduction, role and nature of interview, obtain consent and explain process, obtain permission prior to physical examination.</p><p><strong>Conclusions : </strong>In conclusion, the doctor-patient communication among residents in Internal Medicine Polyclinic At RSUP Dr. Mohammad Hoesin Palembang.</p>

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)0.000
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 score0.166
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.394
Teacher spread0.353 · 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
Published2020
Admission routes1
Has abstractyes

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