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Record W2152613712 · doi:10.12927/hcpap.2007.18754

Reflections on Canada's Public Health Enterprise in the 21st Century

2007· letter· en· W2152613712 on OpenAlexaffvenueabout
Larry W. Chambers, Shannon M. Sullivan

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2007
Typeletter
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsÉlisabeth Bruyère Hospital
Fundersnot available
KeywordsPublic healthHealth carePopulation healthEquity (law)Health policyPolitical sciencePublic policyLibrary sciencePublic administrationSociologyManagementMedicineNursing

Abstract

fetched live from OpenAlex

In their paper, "The Public Health Enterprise: Examining Our Twenty-First-Century Policy Challenges," Tilson and Berkowitz outline six challenges for the United States public health enterprise: infrastructure, essential services, preparedness, accountability and measurement, workforce and the research agenda. Canada also has challenges in these areas. This paper briefly outlines examples of what is being done to respond to these challenges, the current state of public health in Canada and directions being taken in Canada for the future. There are striking similarities in the public health system challenges facing the United States and Canada, despite major differences in organization and financing of healthcare in the two countries. Planning, implementation and evaluation of public health approaches require different management skills and knowledge than for personal healthcare. Both countries must keep up their recent momentum to improve their official public health infrastructure, agreement on essential services, emergency preparedness, accountability and measurement, workforce and research agenda.

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)0.010
metaresearch head score (Gemma)0.026
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 score0.918
Threshold uncertainty score0.593

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0300.013
Scholarly communication0.0140.007
Open science0.0050.005
Research integrity0.0750.059
Insufficient payload (model declined to judge)0.0080.001

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 head0.146
GPT teacher head0.440
Teacher spread0.295 · 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

Citations8
Published2007
Admission routes3
Has abstractyes

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