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Record W4386553347 · doi:10.2519/jospt.2023.11917

Drawing a “Perfect Circle”: How Clinicians Can Become Better Communicators

2023· article· en· W4386553347 on OpenAlexaff
Seth Peterson, Maxi Miciak, Michelle J. Kleiner, John Woolf, Todd E. Davenport

Bibliographic record

VenueJournal of Orthopaedic and Sports Physical Therapy · 2023
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsLondon Health Sciences CentreWestern UniversityUniversity of Alberta
Fundersnot available
KeywordsMindfulnessCuriosityNature versus nurtureContemplationMedicineContext (archaeology)Reflection (computer programming)Medical educationReflective practicePsychotherapistEngineering ethicsPsychologyPedagogyEpistemologySocial psychologyClinical psychologyComputer science

Abstract

fetched live from OpenAlex

SYNOPSIS: Despite the importance of communication in person-focused care, biomedical knowledge and technical skill development are often prioritized in physical therapy education. As clinicians and educators, we contend that mindfulness and reflection nurture effective communication approaches and support physical therapists in navigating the complexity and uncertainty that comprise most clinical interactions. We suggest that clinicians be mindful of the self, the patient, and the context when interacting with patients. Although being mindful cultivates awareness and curiosity, being reflective is an active practice that can be used while deliberating about the right thing to do or say in a particular situation. In this Viewpoint, we offer clinicians and educators suggestions for engaging in mindful and reflective practices. Through the contemplative practices of mindfulness and reflection, clinicians can better cultivate their communication expertise and good practice. J Orthop Sports Phys Ther 2023;53(10):579-584. Epub: 8 September 2023. doi:10.2519/jospt.2023.11917

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)0.045
metaresearch head score (Gemma)0.175
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: Methods · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.175
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0150.024
Scholarly communication0.0200.040
Open science0.0040.018
Research integrity0.0170.029
Insufficient payload (model declined to judge)0.0190.013

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 head0.043
GPT teacher head0.348
Teacher spread0.305 · 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
GenreMethods

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

Citations2
Published2023
Admission routes1
Has abstractyes

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