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Record W4300963335 · doi:10.47326/ocsat.2022.03.68.1.0

Brief on Primary Care Part 2: Factors Affecting Primary Care Capacity in Ontario for Pandemic Response and Recovery

2022· report· en· W4300963335 on OpenAlexfundaboutno aff
Dee Mangin, Kamila Premji, Imaan Bayoumi, Noah Ivers, Azza Eissa, Sarah Newbery, Liisa Jaakkimainen, Dominik Alex Nowak, Michael Green, Suzan Beazley, Andrew D. Pinto, Victoria Haldane, Elizabeth Muggah, Jennifer Rayner, David M. Kaplan, Onil Bhattacharyya, Allan Grill, Nicolas S. Bodmer, Anna Perkhun, Beth Cowper-Fung, Riva E. Levitan, Cathy Mastrogiacomo, Steve Nastos, Prabhjot Sangha, Matthew J. Schurter, James D. Wright, José M Valderas, Karen Born, Fahad Razak, Richard H. Glazier, Tara Kiran, Danielle Martin

Bibliographic record

Venuenot available
Typereport
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
FundersUniversity of TorontoMcMaster University
KeywordsPrimary carePandemicPrimary (astronomy)MedicineCoronavirus disease 2019 (COVID-19)Family medicine

Abstract

fetched live from OpenAlex

Primary care is a crucial component of pandemic and health emergency preparedness, response, and recovery. It is also essential to continued health system improvement, person-centred care in communities, and optimal population health for Ontarians. A capacity crisis in primary care has deepened during the COVID-19 pandemic. Urgent efforts are needed to address the factors that limit primary care provision. This will include ensuring an infrastructure that supports coordinated and integrated primary care. It will also include ensuring the training, support, and retention of interdisciplinary health human resources (HHR) that comprise teams providing care associated with patient enrolment models (PEMs), so they are equitable and accessible for all Ontarians before, during, and after public health emergencies.

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)0.001
metaresearch head score (Gemma)0.004
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: Other · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0240.002

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 head0.193
GPT teacher head0.405
Teacher spread0.212 · 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
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

Citations26
Published2022
Admission routes2
Has abstractyes

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