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Record W2987679365 · doi:10.1097/acm.0000000000003083

On Time and Tea Bags: Chronos, Kairos, and Teaching for Humanistic Practice

2019· article· en· W2987679365 on OpenAlexaff
Arno K. Kumagai, Thirusha Naidu

Bibliographic record

VenueAcademic Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsThe Wilson Centre
Fundersnot available
KeywordsKairosTeachable momentContext (archaeology)WonderPsychologyPedagogySociologyEpistemologyPhilosophySocial psychologyPsychoanalysis

Abstract

fetched live from OpenAlex

In these days of overwhelming clinical work, decreased resources, and increased educational demands, time has become a priceless commodity. Competency-based medical education attempts to address this challenge by increasing educational efficiency and decreasing the "steeping" of learners in clinical activities for set durations of time. However, in this environment, how does one teach for compassionate, humanistic practice? The answer arguably lies in clinician-teachers' recognition and engagement in a different type of time, that of kairos. Ancient Greek thought held that there were 2 interrelated types of time: chronological, linear, quantitative time-chronos-and qualitative, opportune time-kairos. Unlike chronos, kairos involves a sense of the "right time," the "critical moment," the proportionate amount. Developing a sense of kairos involves learning to apply general principles to unique situations lacking certainty and acting proportionally to need and context. Educationally, it implies intervening at the critical moment-the moment in which a thoughtful question, comment, or personal expression of perplexity, awe, or wonder can trigger reflection, dialogue, and an opening up of perspectives on the human dimensions of illness and medical care. A sensibility to kairos involves an awareness of what makes a moment "teachable," an understanding of chance, opportunity, and potential for transformation. Above all, inviting kairos means grasping an opportunity to immerse oneself and one's learners-even momentarily-into an exploration of patients and their stories, perspectives, challenges, and lives.

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)0.011
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0150.063
Scholarly communication0.0150.024
Open science0.0020.013
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0070.001

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 head0.014
GPT teacher head0.374
Teacher spread0.359 · 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
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".

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Citations22
Published2019
Admission routes1
Has abstractyes

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