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Record W1641758495 · doi:10.5750/ejpch.v1i2.688

Cultivating person-centered medicine in future physicians

2013· article· en· W1641758495 on OpenAlexaff
Kevin Garneau, Thomas Hutchinson, Patricia L. Dobkin

Bibliographic record

VenueEuropean Journal for Person Centered Healthcare · 2013
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsBurnoutMindfulnessCompassionMeditationNarrative medicineNarrativePsychologyMedical educationEmpathyMedicineCompassion fatigueAlternative medicineNursingClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Person-centered medicine, while valued implicitly, is not always taught explicitly in medical schools or during residency programs. Threats to educating and practicing person-centered medicine include perceived lack of time, stress, burnout and a paucity of mentors with a systematic approach to modeling and teaching students how to relate to patients in a way that addresses them as whole persons. Herein we review how trainee stress and burnout negatively impact patient care and outline a program designed to teach mindful medical practice that may be an antidote to these problems. Moreover, we present quantitative data and a student’s narrative to highlight how to cultivate person-centered medicine in trainees.Fifty-eight 4th year medical students completed questionnaires pertaining to: depression, burnout, stress, wellbeing, self-compassion and mindfulness before and after taking a 4-week elective entitled, Mindful Medical Practice. Statistically significant improvements were found on emotional exhaustion, depression, self-compassion and mindfulness. One student’s experiences highlighted how what he learned in the elective guided him during his family medicine residency. We conclude with a discussion of how the culture of medicine and the training of future physicians in particular, need to take the whole persons of both the physician and patient into account in order for all to be satisfied with and benefit from medical care.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.110
GPT teacher head0.349
Teacher spread0.239 · 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 designQualitative
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

Citations39
Published2013
Admission routes1
Has abstractyes

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