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Record W2332842330 · doi:10.1097/acm.0b013e31826d6ab3

Beyond the Biomedical Feedlot

2012· article· en· W2332842330 on OpenAlexaffabout
Cynthia Whitehead, Ayelet Kuper

Bibliographic record

VenueAcademic Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurriculumMedical educationSchema (genetic algorithms)Engineering ethicsPsychologyPublic relationsMedicinePedagogyPolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

Medical trainees are force-fed a thick stew of biomedical facts, lightly seasoned with a smattering of professionalism and communication skills. Sated on this rich diet, they have little energy to explore the world beyond. If we want to ensure that our learners develop to their full potential, we must consider how best to guide them along a path to become healers who are thoughtful, reflective, caring, compassionate, and critical thinkers. To do so, we will need to encourage them to journey beyond the biomedical feedlot. While we must obviously ensure that we do not send unskilled, inept, or incompetent practitioners into the workplace, most medical educators aspire to help our students develop beyond this minimum standard. A curriculum focused on only biomedical facts and clinical competencies is unlikely to ensure this broader development, and yet we increasingly seek efficient ways to move our learners along a factory production line of training. In a recent JAMA article, for example, Emanuel and Fuchs1 threw down a gauntlet to medical educators by calling for a 30% reduction in medical training time by 2020. Commenting that “[w]aste, especially wasting the time of some of society’s most highly educated and talented people, is unethical,” they exhort educators to “focus attention on the essential content of medical training.”1 They propose doing away with comprehensive training in favor of creating specialist clinician–technicians who meet a limited number of specific competency standards. Their schema focuses on what they deem to be essential content: a focused list of preclinical science content areas and clinical skills. While most medical educators would not currently make such a drastic efficiency argument, some certainly sympathize with the idea of stripping medical education back to a basic, more manageable core. Yet, we would argue that learners will never develop to their full potential unless educators step back from adjusting content checklists of facts and skills and instead revisit how learners know and think. It has long been accepted that being a good doctor requires far more than biomedical expertise. Hearty meat-and-potatoes bioscientific fare without vegetables and vitamins from the social sciences and humanities will produce neither healthy nor happy results for physicians or patients. Medical educators clearly recognize that optimal medical student development requires a balanced diet, combining biomedical knowledge with other important areas. This understanding is formally acknowledged in current competency standards. In the United States, the Medical Knowledge competency is only one of six Accreditation for Graduate Medical Education Core Competencies; in Canada, Medical Expert is one of seven in the CanMEDS Physician Competency Framework. A curriculum that actually taught the knowledge required for the other competencies would draw heavily on the social and behavioral sciences and humanities.2 However, the 20th century saw a privileging of biomedical knowledge that strongly limited the incorporation of the other extremely important forms of knowledge into the curriculum. Recently, organizations such as the Association of American Medical Colleges have begun to formally articulate the need for nonbiomedical forms of knowledge in medical curricula.3 There nonetheless remains a deeply rooted tendency to consider biomedical expertise as based in “facts” and to dismiss other important areas of physician competence (communication, collaboration, professionalism) as “soft skills” that do not require similar groundings in appropriate forms of knowledge. In addition to accommodating other forms of knowledge, it has long been a struggle to incorporate even new biomedical knowledge into an always over-full medical curriculum. Despite its position as privileged knowledge, biomedicine is thus frequently badly taught as a long series of facts, a process that permits student engagement with the materials as data to be memorized rather than as ideas to be grappled with intellectually. Framed in this way, biomedical learning turns into the stuff of late-night brain stuffing—surely not the way to encourage fulsome development! Instead, we must find ways to inculcate the sense that biomedical knowledge requires inquisitive observation and interpretation, incorporating Flexner’s notion of a good doctor as a thoughtful and curiosity-driven scientist.4 Enabling learners to develop to their full potential will require a significant shift from current approaches both to forms of knowledge and to the ways we expect learners to engage with that knowledge. While finding a way to ensure minimum competency standards, we must consider that step as merely meeting our learners’ basic dietary requirements. We must also provide them with sufficient sustenance to set out on a lifelong journey of exploration of the art and science of medicine, stimulating their intellectual curiosity about the wonders that make up medical knowledge.

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)0.008
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.044
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.013
Scholarly communication0.0130.017
Open science0.0020.014
Research integrity0.0080.017
Insufficient payload (model declined to judge)0.0440.015

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 head0.035
GPT teacher head0.381
Teacher spread0.347 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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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Citations6
Published2012
Admission routes2
Has abstractyes

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