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Record W2791345749 · doi:10.1136/bmj.k1160

Quebec doctors reject pay rise in favour of investment in health system

2018· article· en· W2791345749 on OpenAlexaboutno aff
Barbara Kermode-Scott

Bibliographic record

VenueBMJ · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsRemunerationRestructuringPaymentGovernment (linguistics)Health careInvestment (military)BusinessPublic administrationPolitical scienceLawFinancePolitics

Abstract

fetched live from OpenAlex

Hundreds of doctors in Quebec are turning down pay rises, saying that they are too embarrassed to accept the higher payments while colleagues and patients are struggling in a health system beset by years of drastic cuts and restructuring. The doctors have asked the provincial government to instead invest the money in improving Quebec’s health system. “The only thing that seems to be immune to cuts is our remuneration,” states the advocacy group Quebec Doctors for Public Health Care (known by the acronym MQRP) in a letter posted online on 25 February to …

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)0.009
metaresearch head score (Gemma)0.041
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: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.406

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0090.006
Scholarly communication0.0090.003
Open science0.0010.002
Research integrity0.0140.017
Insufficient payload (model declined to judge)0.0210.003

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 head0.063
GPT teacher head0.316
Teacher spread0.253 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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