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BMI IN THE OLD ORDER AMISH: RESPONSE

2004· article· en· W2090617906 on OpenAlexaboutno aff
David R. Bassett, Patrick Schneider, Gertrude E. Huntington

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsnot available
Fundersnot available
KeywordsObesityAgrarian societyDemographyPopulationGerontologyBody mass indexAgricultureGeographyScarcityMedicineSocioeconomicsSociologyArchaeologyEconomics

Abstract

fetched live from OpenAlex

Dear Editor-in-Chief: Drs. Snitker and Shuldiner present some fascinating data on body mass index (BMI) and diabetes prevalence in Old Order Amish living in Lancaster County, Pennsylvania (2,5). As they note, some of the disparity in BMI between their study population and ours may be attributable to a difference in the mean age of the groups. Differences in recruitment methods, genetic predisposition to obesity, and dietary intake also cannot be ruled out. We continue to believe, however, that the scarcity of obesity among the Amish adults we studied is related to their active lifestyle. This Amish community in Ontario, Canada, was specifically chosen because we wanted to study the physical activity habits of a traditional, agrarian group whose lifestyle is similar to that of most North Americans 150 yr ago (1). This group is one of the more technologically conservative branches of the Old Order Amish. Not all Amish communities are the same, and considerable differences in lifestyle and in the adoption of modern technology exist. In Ohio, Indiana, and Pennsylvania farmland has become very expensive (4). As a result, some farms have been subdivided into smaller plots, and many Amish have resorted to operating cottage industries and small businesses. These include woodworking, cabinetry, metalworking, and crafts. Some Amish run bakeries, tourist shops, and tax accounting practices (4). We observed the presence of microindustries in Ontario, but they were scarce. The proportion of Amish men who made their living from farming was 78% in our study, compared with only 40% in the Lancaster County study (2). Even among Amish who farm, there are differences in the extent to which modern technology is used in working the land. The Ontario Old Order Amish do not use farm equipment (forage choppers, corn pickers, hydraulic plows) that might be allowed in less strict Amish communities. We also saw no evidence of mechanical gutter cleaners, storage and cooling tanks for milk, milking machines, and forklifts that are allowed in more mechanized Amish communities (3). In the home, the Ontario Old Order Amish also favor the old-fashioned approach. They use kerosene lanterns and rely on iceboxes rather than propane or kerosene refrigerators. They do not have continuous hot water, though they do have indoor toilets, bathtubs, and gasoline-powered wringer washing machines. Personal telephones in the home are not permitted. Even bicycles and roller blades are forbidden, although these might result in substantial physical activity! The disparity in the extent to which modern technology has been adopted by various Amish communities and other Anabaptist groups (e.g., Mennonites and Hutterites) presents an opportunity for studying how technology impacts health and fitness. Standardizing methods used in data collection would allow for such comparisons. David R. Bassett, Jr. Patrick Schneider University of Tennessee, Knoxville, TN Gertrude E. Huntington University of Michigan Ann Arbor, MI

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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 designObservational
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

Citations6
Published2004
Admission routes1
Has abstractyes

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