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Record W4391105848 · doi:10.1186/s12961-023-01100-8

A novel methodological approach to participant engagement and policy relevance for community-based primary medical care research during the COVID-19 pandemic in Australia and New Zealand

2024· article· en· W4391105848 on OpenAlexaff
Katelyn Barnes, Sally Hall Dykgraaf, Kathleen O’Brien, Kirsty Douglas, Kyle Eggleton, Nam Bui, Sabrina T. Wong, Rebecca Etz, Felicity Goodyear‐Smith

Bibliographic record

VenueHealth Research Policy and Systems · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of British Columbia
FundersAgency for Healthcare Research and QualityMinistry for Business Innovation and EmploymentAndrew and Corey Morris-Singer FoundationSamueli Institute
KeywordsPandemicHealth services researchRelevance (law)Coronavirus disease 2019 (COVID-19)Public healthHealth administrationHealth policyPrimary care2019-20 coronavirus outbreakSocial policyCommunity engagementMedicinePolitical scienceNursingFamily medicinePublic relationsVirologyDisease

Abstract

fetched live from OpenAlex

Community-based primary care, such as general practice (GP) or urgent care, serves as the primary point of access to healthcare for most Australians and New Zealanders. Coronavirus disease 2019 (COVID-19) has created significant and ongoing disruptions to primary care. Traditional research methods have contributed to gaps in understanding the experiences of primary care workers during the pandemic. This paper describes a novel research design and method that intended to capture the evolving impact of the COVID-19 pandemic on primary care workers in Australia and New Zealand. Recurrent, rapid cycle surveys were fielded from May 2020 through December 2021 in Australia, and May 2020 through February 2021 in New Zealand. Rapid survey development, fielding, triangulated analysis and dissemination of results allowed close to real-time communication of relevant issues among general practice workers, researchers and policy-makers. A conceptual model is presented to support longitudinal analysis of primary care worker experiences during the COVID-19 pandemic in Australia and New Zealand, and key learnings from applying this novel method are discussed. This paper will assist future research teams in development and execution of policy-relevant research in times of change and may inform further areas of interest for COVID-19 research in primary care.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.087
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.634
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0870.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0000.000

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 head0.923
GPT teacher head0.706
Teacher spread0.217 · 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

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative · Other design
DomainMethods
GenreMethods

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

Citations3
Published2024
Admission routes1
Has abstractyes

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