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Record W2017669693 · doi:10.3138/cbmh.18.1.67

Health Issues and the Pala Indian Reservation, 1903–20

2001· article· en· W2017669693 on OpenAlexvenueno aff
Joel R. Hyer

Bibliographic record

VenueCanadian Journal of Health History · 2001
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReservationTraditional medicineHistoryMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

Joel R. Hyer investigates health conditions on one Indian reservation in Southern California during the first two decades of the twentieth century. Hyer contends that unsanitary conditions on the Pala Reservation actually facilitated the spread of diseases among local Cupeño, Luiseño, and Kumeyaay Indians. He also describes how the United States federal government employed doctors, field matrons, and others to promote good health and combat disease among Indians at Pala. The author asserts that, despite their altruistic intentions, some of these government workers attempted to discourage local indigenous peoples from consulting their shamans for medical attention - Native healers whose extensive knowledge of roots and herbs had cured many forms of illness for years. In addition, the teacher at the reservation's day school sought to prevent the spread of disease among her Indian pupils by exposing them to American modes of health care. Furthermore, Hyer maintains that one national health program, the "Save the Babies" campaign, was successful on the Pala Reservation because Indian mothers believed in it and followed specific guidelines to lower mortality rates among their own children. Throughout his essay, the author addresses the pressures of assimilation and acculturation that were so accute at this time in the United States. Hyer concludes by suggesting that many of his findings reflect broader health trends on Indian reservations throughout the United States during this period.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.366
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
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 head0.113
GPT teacher head0.402
Teacher spread0.288 · 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.

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

Citations3
Published2001
Admission routes1
Has abstractyes

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