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Developing a new line of patter: can doctors change their consultations for sore throat?

2002· article· en· W1986844369 on OpenAlexaff
Stephen Rollnick, Clive Seale, Paul Kinnersley, Maggs Rees, Christopher Butler, Karenza Hood

Bibliographic record

VenueMedical Education · 2002
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster University
FundersOffice of Research and Development
KeywordsSore throatMedicineIntervention (counseling)Simulated patientGeneral practiceFamily medicineNursingSurgery

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Doctors report pressure from peers to reduce prescribing of antibiotics for minor respiratory illnesses, and from patients to do the opposite. It has been suggested that doctors adopt a more patient-centred consulting style in order to encourage patient satisfaction and shared decision-making. No evidence exists that such changes are achievable. We developed a new, on-site method for training postgraduates and used this for teaching patient-centred intervention. Here, we examine whether this training method is associated with changes in consulting patterns in consultations for sore throat with children, among doctors from a single group practice. METHODS: Audiotaped consultations (simulated and real) conducted before and after training were analysed and interviews were carried out with participants about the impact of training. SETTING: A general practice in South Wales. PARTICIPANTS: Four general practitioners who consulted with 25 real and simulated patients participated in the study. MAIN OUTCOME MEASURES: Four patient-centred skills used by doctors and 2 patient behaviours measured before and after training were identified. RESULTS: Three out of 4 practitioners produced clear evidence of changes in patient-centred consulting skills. These changes were evident in simulated and real consultations 2 and 4 weeks later, respectively. Prior to training the doctors produced only five examples of patient-centred skills in 10 consultations. After training they produced 39 examples in 15 consultations. CONCLUSIONS: Evidence from both consultations and interviews indicated that the intervention and training were well received and had been put into practice.

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)0.004
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.431
GPT teacher head0.492
Teacher spread0.062 · 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 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

Citations22
Published2002
Admission routes1
Has abstractyes

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