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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 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0010.000

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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations26
Published2022
Admission routes2
Has abstractyes

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