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Record W1984445931 · doi:10.1097/acm.0b013e318189286e

On Water Buffalo and Academic Medicine

2008· article· en· W1984445931 on OpenAlexaboutno aff
Ming‐Jung Ho

Bibliographic record

VenueAcademic Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsHumanismChristian ministryPopulationMedical educationSociologyMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

Beginning with the Han Dynasty, around 100 BC, water buffalos played a pivotal role in Chinese civilization, when they began to be used in plowing rice fields. Heavier plows and increased food production led to significant population growth. The admiration of the water buffalo in Chinese societies is evident in abundant artistic portraits as well as in the prevalent aversion to eating beef. What has a water buffalo to do with academic medicine? The revelation dawned on me about a year ago when I became involved in a project promoting humanism in medicine. For those readers unfamiliar with Taiwanese medical education, the Flexner model of medical education has had great influence. Although basic science knowledge and clinical skills are emphasized in Taiwanese medical schools, recently an insightful advisor to the Ministry of Education urged the ministry to devote resources to restoring the centrality of humanities in Taiwanese medical education. Consequently, a renowned senior faculty member in my department was given a grant, and junior faculty members were required to share the work. Since I was educated in liberal arts, medicine, and anthropology in the United States and the United Kingdom, the lion's share of the task of integrating the humanities and social sciences into medical education was bestowed upon me. As a student of anthropology, I have observed the Taiwanese academic medicine culture in which junior faculty members obediently address each senior faculty member as “seinsei” (teacher) and carry out research projects commissioned to the seinsei. However, I was relatively new to the Taiwanese medical schools and was not sure whether I should “go native” and take on the heavy burden. I started to investigate the scope of the project by conducting in-depth interviews with the leadership of the 11 Taiwanese medical schools, gathering their perspectives on medical humanities education. I sought advice from a trusted dean on what a junior faculty member with two small children should do when a superior assigns her a grand task while she is still struggling to establish a foothold in academic medicine. The dean smiled and pointed to a ceramic sculpture of a water buffalo pulling a cart full of crops on the coffee table. “See this water buffalo, my favorite animal? It keeps on pulling the cart no matter how heavy the load is. Of course, if you cannot bear any more weight, you should politely let the senior know. However, it is better to work in teams. In addition, you should feel honored to be invited to join a team.” The image of the ceramic water buffalo has stayed in my mind since I began to work on the project to restore humanism in medical education. After conducting needs assessments of faculty members and students across the country, I identified faculty development to be the critical first step. Over the past year, with the assistance of experienced medical educators from distinguished institutions such as Harvard, Johns Hopkins, UCSF, Brown, McGill, and McMaster, a series of workshops have engaged over 300 medical educators from the 11 Taiwanese schools to begin to plan the longitudinal integration of the humanities and social sciences. A dozen projects have been commissioned to Taiwanese medical educators to develop and implement relevant curriculum. More workshops and projects will be planned in the next three years. My outlook is hopeful as I begin to recognize more water buffalos in academic medicine. Together, we diligently plow the fields, turning over the surface soil to bring nutrients and aerating the soil to allow it to hold more moisture. Perhaps, one of us will survive and evolve into a seinsei figure, recruiting more water buffalos, expanding the fields, or inventing the tractor.

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)0.004
metaresearch head score (Gemma)0.007
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0100.020
Scholarly communication0.0080.012
Open science0.0020.014
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0330.004

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 head0.060
GPT teacher head0.356
Teacher spread0.297 · 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
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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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