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Record W2138276478 · doi:10.1080/01421590701753443

Do patients’ comfort levels and attitudes regarding medical student involvement vary across specialties?

2008· article· en· W2138276478 on OpenAlexaff
Kavitha Passaperuma, Jennifer Higgins, Stephanie Power, Tamsen E. Taylor

Bibliographic record

VenueMedical Teacher · 2008
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsWestern University
Fundersnot available
KeywordsSpecialtyMedicineFamily medicineObstetrics and gynaecologyPreferencePregnancy

Abstract

fetched live from OpenAlex

BACKGROUND: Studies on patient comfort with medical student involvement have been conducted within several specialties and have consistently reported positive results. However, it is unknown whether the intrinsic differences between specialties may influence the degree to which patients are comfortable with student involvement in their care. AIM: This is the first study to investigate whether patient comfort varies across specialties. METHODS: A total of 625 patients were surveyed in teaching clinics in Family Medicine, Obstetrics/Gynaecology, Urology, General Surgery, and Paediatrics. Seven patient attitudes and patients' comfort levels based on student gender, level of training, and type of clinical involvement were assessed. RESULTS: Patients in all specialties shared similar comfort levels and attitudes regarding medical student involvement for the majority of parameters assessed, suggesting that findings in this area may be generalised between specialties. Most of the inter-specialty variation found pertained to patient preference for student gender and the genitourinary specialties. CONCLUSION: As there are numerous specialties that have never undergone a similar investigation of their patients, this study has important implications for medical educators in those specialties by supporting their ability to apply the results and recommendations of studies conducted in other specialties to their own.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.315
GPT teacher head0.491
Teacher spread0.176 · 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

Citations68
Published2008
Admission routes1
Has abstractyes

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