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Record W2747827529 · doi:10.1108/he-01-2017-0011

Physician verbal compliance-gaining strategies and patient satisfaction

2017· article· en· W2747827529 on OpenAlexaff
Janna Olynick, Alexandra Iliopulos, Han Z. Li

Bibliographic record

VenueHealth Education · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsPersuasionCompliance (psychology)Patient satisfactionMedicineFamily medicinePatient complianceMedical advicePsychologyNursingSocial psychology

Abstract

fetched live from OpenAlex

Purpose The patient healthcare experience is a complex phenomenon, as is encouraging patient compliance with medical advice. To address this multifaceted relationship, the purpose of this paper is to explore the ways resident physicians verbally encourage patient compliance and the relationship between these compliance-seeking strategies and patient satisfaction. Design/methodology/approach A total of 40 medical interviews between resident physicians and patients were audio-recorded, transcribed, coded, and analysed. Patient questionnaires were also administered and analysed. Findings It was found that resident physicians used indirect orders most frequently, followed by motivation, persuasion, scheduling, and direct orders. It was also found that female patients received (marginally) more messages than male patients; female residents used more messages with female patients than with male patients; female residents used more persuasion messages with female patients than with male patients; male residents were less likely than female residents to use motivational messages with female patients; and compliance was significantly correlated with expertise satisfaction, overall satisfaction, and communication satisfaction. Originality/value This study advances existing research by examining various ways in which residents verbally encourage patient compliance and the relationship between these messages and patient satisfaction. Findings can be used to inform physicians on strategies to encourage patient adherence to medication regimen, appointments, and lifestyle changes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
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 head0.370
GPT teacher head0.512
Teacher spread0.142 · 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.

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

Citations10
Published2017
Admission routes1
Has abstractyes

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