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Record W2604408325

Accepting new patients

2011· article· en· W2604408325 on OpenAlexvenueaboutno aff
Roger Chafe, Andreas Laupacis, Wendy Levinson

Bibliographic record

VenueCanadian Family Physician · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsExcuseMedicineSession (web analytics)Public relationsOrder (exchange)Political scienceMedical educationFamily medicineLawBusiness
DOInot available

Abstract

fetched live from OpenAlex

Objective To gauge the public’s opinion of the College of Physicians and Surgeons of Ontario’s (CPSO’s) policy on how primary care physicians should accept new patients. Design Deliberative citizens’ council. Setting Toronto, Ont. Participants Twenty-five public members of the Toronto Health Policy Citizens’ Council. Methods A 2-day council session was held, during which the new policy was presented and council members heard from experts with various perspectives on the issues involved. Council members then deliberated and developed recommendations concerning the policy. Main findings Council members agreed that a first-come, first-served policy was an appropriate method for family physicians to use when accepting new patients. They thought the policy’s exception, which allows physicians not to accept patients based on a lack of clinical competency in an area, should be clarified in order to avoid it being used as an excuse to inappropriately screen patients. Counsel members also encouraged the CPSO to publicize its policy as widely as possible, so that potential patients undergoing screening in the future will recognize that this goes against the CPSO’s policy and can take appropriate action if they wish. Conclusion How family physicians accept new patients into their practices is a sensitive issue. The CPSO policy provides guidance on how new patients should be admitted, which, if it is appropriately enacted, seems reasonable to informed members of the public.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.998

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)0.0000.003

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 head0.096
GPT teacher head0.232
Teacher spread0.136 · 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
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
Published2011
Admission routes2
Has abstractyes

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