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Record W1995988132 · doi:10.13162/hro-ors.v3i1.1177

Expanding the Scope of Practice for Pharmacists in Ontario

2015· article· fr· W1995988132 on OpenAlexaffvenueabout
Glen E. Randall, Neil G. Barr, Patricia A. Wakefield, Mark Embrett

Bibliographic record

VenueHealth Reform Observer - Observatoire des Réformes de Santé · 2015
Typearticle
Languagefr
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScope (computer science)Scope of practiceBusinessPolitical scienceComputer scienceHealth care

Abstract

fetched live from OpenAlex

the Regulated Health Professions Statute Law Amendment Act, 2009 (Bill 179) received Royal Assent in Ontario. The resulting legislative amendments were intended to strengthen government oversight of the health regulatory colleges, promote interprofessional collaboration, and make better use of health professionals' existing skills and training by enhancing the scope of practice for several health professions in order to improve health system efficiency. Among the most notable scope of practice enhancements were those given to pharmacists, who would be permitted to: modify and renew existing prescriptions, prescribe a limited range of drugs independent of a physician, and administer medications such as vaccinations via injection or inhalation. The reform was driven in large part by the government's concerns related to the rising cost of health care, the public's desire for greater access to services, and demonstrated successes of similar reforms in other jurisdictions. While the Ontario reform has had some clear success, such as expanding the public's access to influenza vaccinations, to date, the evidence of achieving other goals remains weak. In particular, there is no clear evidence of improved health system efficiency and associated cost effectiveness. Moreover, it is possible that Ontario's umbrella regulatory model may be making interprofessional collaboration more, rather than less, difficult to realize.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.306
GPT teacher head0.473
Teacher spread0.167 · 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 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
Published2015
Admission routes3
Has abstractyes

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