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Record W4406174489 · doi:10.3138/9781487560454

Tommy Douglas and the Quest for Medicare in Canada

2024· book· en· W4406174489 on OpenAlexaboutno aff
Gregory P. Marchildon

Bibliographic record

VenueUniversity of Toronto Press eBooks · 2024
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceGerontologyMedicine

Abstract

fetched live from OpenAlex

"How and why was universal health coverage implemented so early in a poverty-stricken province in Canada? Why was its design so faithfully replicated in the national standards that ultimately shaped Medicare across the rest of Canada? Seeking to answer these questions, Tommy Douglas and the Quest for Medicare in Canada explores the history of universal health care through the life of Canadian politician Tommy Douglas, identifying the pivotal moments and decisions that led to the establishment of Medicare in Canada. The book traces the origins of Medicare back to the 1930s Depression and its devastating impact on the Prairie populations. Marchildon examines how Tommy Douglas and a new generation of reformers, radicalized by the Depression, prioritized socialized health care. The book reveals how, as the provincial party leader, Douglas leveraged support from both local and external allies to rapidly implement universal hospital insurance and lay the groundwork for a new health system. Despite strong opposition from physician and business lobbies, Douglas continued to pressure the government for federal cost-sharing of universal health coverage. Drawing on archival sources including speeches, television broadcasts, and cabinet documents, Tommy Douglas and the Quest for Medicare in Canada illuminates how Douglas's vision, leadership, and coalition-building among unions were crucial to the successful establishment of Medicare in Canada."--

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.900
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.027
GPT teacher head0.206
Teacher spread0.179 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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