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Record W2518567342 · doi:10.2147/jmdh.s107851

Patient-centeredness and empathy in a bilingual interprofessional primary care teaching clinic: a pilot study

2016· article· en· W2518567342 on OpenAlexaff
Allison A. Vanderbilt, Sallie D. Mayer, Erika Peterfy, Steven H. Crossman, Lisa Phipps

Bibliographic record

VenueJournal of Multidisciplinary Healthcare · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsInstitute of Population and Public Health
FundersVirginia Commonwealth University
KeywordsEmpathyFeelingFree clinicMedicinePatient satisfactionPrimary careNormativeFamily medicinePsychologyMedical educationNursingHealth carePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Utilizing the Consultation and Relational Empathy survey, this project examined the perceptions of care team empathy and patient-centeredness between English- and Spanish-speaking patients. From fall through spring semesters, patient surveys from a primary care, interprofessional student-led teaching clinic were collected and analyzed. Overall, mean scores for both English- and Spanish-speaking patients were above the reported normative average for general practitioners. While, overall, patients expressed satisfaction with the student-led teaching clinic in terms of empathy and patient-centeredness, English-speaking patients had higher median scores than Spanish-speaking patients. Analyzed individually, questions related to communication and provider attitudes were scored lower by Spanish-speaking patients. These results demonstrate that student-led clinics can deliver patient-centered care and highlight the continuing need to investigate and address disparities between English- and Spanish-speaking patients with regard to feelings of empathy and patient-centeredness.

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)0.003
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.084
GPT teacher head0.461
Teacher spread0.377 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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