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The Administrative Policy Quandary in Canada’s Health Service Organizations

2013· book-chapter· en· W2478169287 on OpenAlexaffabout
Grace I. Paterson, Jacqueline M. MacDonald, Naomi Nonnekes Mensink

Bibliographic record

VenueAdvances in healthcare information systems and administration book series · 2013
Typebook-chapter
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsHealth CanadaDalhousie University
Fundersnot available
KeywordsHealth policyBusinessService (business)Administrative services organizationPublic relationsNova scotiaHealth careHealth informaticsPublic administrationPolitical scienceMarketingSociology

Abstract

fetched live from OpenAlex

This chapter examines the process for administrative health service policy development with respect to information sharing and decision-making as well as the relationship of policy to decision making. The challenges experienced by health service managers are identified. The administrative health policy experience in Nova Scotia is described. There is a need for integrated policy at multiple levels (public, clinical, and administrative). The quandary is that while working to share health information systems, most Canadian health service organizations continue to individually develop administrative health policy, expending more resources on policy writing than on translation/education, monitoring, or evaluation. By exploring the importance and nature of administrative policy as a foundation for quality improvement in healthcare delivery, a case is made for greater use of health informatics tools and processes.

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)0.005
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.709
Threshold uncertainty score0.823

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0230.013
Scholarly communication0.0200.003
Open science0.0020.003
Research integrity0.0050.006
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 head0.031
GPT teacher head0.370
Teacher spread0.340 · 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
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

Citations0
Published2013
Admission routes2
Has abstractyes

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