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Record W4383814956 · doi:10.12688/mep.19182.2

“Grabbing” Autonomy When the Learning Environment Doesn’t Support it: An Evidence-based Guide for Medical Learners

2023· article· en· W4383814956 on OpenAlex
Adam Neufeld

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedEdPublish · 2023
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAutonomyPsychologyCompetence (human resources)Self-determination theoryInterpersonal communicationPreceptorSocial psychologyPedagogyMedical educationKnowledge managementComputer scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

According to self-determination theory (SDT), environments which support the basic psychological needs for autonomy, competence, and relatedness will facilitate autonomous motivation, learning, and wellness. On the other hand, environments which introduce external controls and power dynamics into the equation will do the opposite. Educational studies support these principles, yet most have focused on learners' need satisfaction as a passive process (e.g., via support or hindrance by educators), rather than the agentic pursuit that SDT emphasizes. In this commentary, I draw on my experience as a practicing physician and SDT researcher, and focus on how medical learners can "grab" more autonomy when the learning environment does not support it. I present a hypothetical case of a preceptor whose teaching style is controlling and unfortunately well-known to medical learners. I then unpack the case and outline different strategies that medical learners can use to navigate this type of interpersonal conflict.

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.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.402
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0480.001

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 head0.071
GPT teacher head0.347
Teacher spread0.276 · 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