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Record W2895696651 · doi:10.12927/hcpap.2018.25579

Physician Service Costs: Is there Blame to Share Around?

2018· letter· en· W2895696651 on OpenAlexaffvenue
Audrey Laporte

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2018
Typeletter
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsBlameProductivityBusinessService delivery frameworkService (business)Quality (philosophy)Product (mathematics)Health careDelivery systemHealthcare deliveryPublic relationsMarketingOperations managementMedicineEconomicsEconomic growthPolitical science

Abstract

fetched live from OpenAlex

The rising portion of national income devoted to healthcare in general and the portion allocated to physician services have been a focus of the health policy literature for some time. Greater recognition should be given to the fact that the observed trends in physician service expenditures are the product of the interaction between physicians and provincial governments. Improving the productivity of healthcare systems in the delivery of high-quality primary care will require moving beyond simple oversight to deeper engagement with physicians as partners in system improvement.

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)0.004
metaresearch head score (Gemma)0.033
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 score0.055
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.033
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0040.007
Open science0.0020.002
Research integrity0.0550.040
Insufficient payload (model declined to judge)0.0140.006

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 head0.091
GPT teacher head0.404
Teacher spread0.313 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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