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Record W2328909763 · doi:10.1097/acm.0000000000000454

Keeping the Human Touch in Medical Practice

2014· letter· en· W2328909763 on OpenAlexaffabout
Martina Kelly, Wendy Tink, Lara Nixon

Bibliographic record

VenueAcademic Medicine · 2014
Typeletter
Languageen
FieldMedicine
TopicHealth Promotion and Cardiovascular Prevention
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNonverbal communicationContext (archaeology)GestureInterpersonal communicationPsychologyReading (process)Physical examinationBody languageFocus (optics)Cognitive psychologyMedicineSocial psychologyCommunicationComputer scienceLinguistics

Abstract

fetched live from OpenAlex

To the Editor: We welcome Gowda and colleagues’1 focus on physical examination and their invitation to engage in more meaningful discussions on its teaching. We highlight the interpersonal nature of physical examination. Although performance of physical examinations is traditionally reported as “on” a patient, we suggest an increased emphasis on physical examination enacted “with” a patient. Physical examination is not only a process of information gathering, technique, or hypothesis testing; it is an important form of communication and a key element in the delivery of patient-centered care. A core element of physical examination is human touch. Touch is a dominant form of nonverbal communication used in clinical care. Although nonverbal communication is addressed in many medical school curricula, the focus tends to be on body language and use of gestures rather than the intimacy of touch. Yet physical examination is a dynamic process of engagement.2 For example, as we examine a patient, we perceive on multiple levels—not just the presence or absence of physical signs, but also the patient’s comfort and emotional state. In turn, the patient responds to us—reading our facial expressions, interpreting the pressure of our fingertips, and responding to the gentleness (or lack thereof) to inform how he or she will proceed within the consultation. This exchange often happens at an unconscious level, yet awareness and attentiveness to these subtleties can, we suggest, make an important contribution to the doctor–patient relationship. We concur with Gowda et al about the importance of context. The majority of patient care is delivered in ambulatory care settings, where a focused examination is likely to be performed, and we welcome as appropriate the earlier introduction of instruction in this approach. Issues of context extend beyond specialty or physical location to incorporate issues of gender, culture, age, and prior experience of patient and doctor. Although these areas may be covered under professionalism, they also represent a form of embodied knowledge and experience that is rarely explicated during clinical skills teaching. We propose a wider consideration of physical examination teaching that moves beyond the technical to include the human experience. Martina Kelly, MB BCh BAO, MA Associate professor, Department of Family Medicine, University of Calgary, Calgary, Alberta, Canada; e-mail: [email protected] Wendy Tink, MD, BSc, FCFP Assistant professor, Department of Family Medicine, University of Calgary, Calgary, Alberta, Canada. Lara Nixon, MD, FCFP Assistant professor, Department of Family Medicine, University of Calgary, Calgary, Alberta, Canada.

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)0.006
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.020
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0080.008
Open science0.0040.003
Research integrity0.0200.027
Insufficient payload (model declined to judge)0.0090.004

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 head0.065
GPT teacher head0.421
Teacher spread0.356 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations25
Published2014
Admission routes2
Has abstractyes

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