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Record W2900441208 · doi:10.1177/0741713618807752

Learning to Be a Sensitive Professional: A Life-Enhancing Process Grounded in the Experience of the Body

2018· article· en· W2900441208 on OpenAlexaff
Josée Lachance, Geneviève Emond, F. Vinit

Bibliographic record

VenueAdult Education Quarterly · 2018
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversité du Québec à MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsGrounded theoryExperiential learningFormative assessmentPsychologyPerceptionContext (archaeology)Process (computing)PedagogyConsciousnessProfessional developmentMedical educationQualitative researchSociologyMedicine

Abstract

fetched live from OpenAlex

This article focuses on the bodily engagement of professionals in the context of formative adult education. It examines how the body can be lived and sensed from different angles, based on two experiential studies: one with student-teachers, in somatic education, and one with physicians, based on Awakening the Sensible Being. The research results are compared to demonstrate how participants engage in their relationships with themselves and with others and how this enriches their coherence. The research suggests that teaching body perception can be as beneficial for teachers and physicians as it is for their students and patients. In our findings, it seems that body awareness and consciousness allow professionals to process information that is not available through other channels, enabling them to offer services that respond more humanely to the demands and needs. With body awareness, they can move toward a more grounded and coherent professional practice.

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0050.017
Scholarly communication0.0050.006
Open science0.0010.012
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.344
Teacher spread0.333 · 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 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

Citations13
Published2018
Admission routes1
Has abstractyes

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