MétaCan
Menu
Back to cohort
Record W2975911762 · doi:10.12927/hcq.2019.25911

Cutting Through the Ice …

2019· article· en· W2975911762 on OpenAlexaffvenue
PG Forest

Bibliographic record

VenueHealthcare Quarterly · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFocus (optics)Public relationsMedicinePsychologyNursingPolitical science

Abstract

fetched live from OpenAlex

The papers that follow are part of an honest, reasonable and serious attempt to build on an existing consensus at the basis of medicare, which guarantees that all Canadians can get medical attention when sick and hospital care when very sick (or injured). Without any exception, reform proposals that run counter to these principles are doomed to failure. However, it becomes harder and harder to ensure that costly and complex healthcare services can be "readily and timely" accessed without a radical shift in approaches. To say things otherwise, to keep what we cherish, we must embrace change, in the form of collaboration, measurement and evidence.

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)0.030
metaresearch head score (Gemma)0.111
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 score0.041
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.111
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.012
Scholarly communication0.0230.028
Open science0.0030.008
Research integrity0.0110.023
Insufficient payload (model declined to judge)0.0410.021

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 head0.056
GPT teacher head0.430
Teacher spread0.374 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueHealthcare QuarterlySame topicPrimary Care and Health OutcomesFrench-language works237,207