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Record W4214876941 · doi:10.1111/1467-9752.12645

Clinical education and philosophically informed reflective practice

2022· article· en· W4214876941 on OpenAlexaff
Alex Cousins

Bibliographic record

VenueJournal of Philosophy of Education · 2022
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsClinical PracticePsychologyModalitiesReflective practiceEpistemologyMedicineMedical educationPsychotherapistEngineering ethicsPedagogySociologyNursingSocial science

Abstract

fetched live from OpenAlex

The common factors hypothesis holds that therapy's effectiveness is not found in features of specific therapeutic methods, but rather that therapy is helpful due to the presence of three general features that all therapeutic modalities share: positive clinician traits, commitment to a common practice to achieve a common goal and effective communication. These features are also cited as crucial competencies of doctors in general. Yet despite the demonstrable importance of these ‘soft skills’, prominent clinical educators are calling for greater understanding and implementation of common factors education. In this paper, I seek to provide an account of what a ‘common factor’ or ‘soft skill’ is, and how to incorporate education of these ‘common factors’ into clinical education. To this end, I argue that common factors are best understood as philosophically informed reflective practice. Current medical accounts of therapy and the education of therapeutic practice lack this understanding, and as a result miss some key conceptual features that philosophically informed reflective practice could provide. I will also demonstrate how philosophically informed reflective practice can be suffused into familiar clinical pedagogical methods. In doing so, I will illustrate how clinical education can be reformed with philosophically informed reflective practice in mind.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.050
GPT teacher head0.435
Teacher spread0.386 · 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 designOther design
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

Citations0
Published2022
Admission routes1
Has abstractyes

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