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Record W4392605535 · doi:10.12927/hcpap.2024.27273

Can Answers to the Health Workforce Crisis Be Found in Equity-Informed Digital Health?

2024· article· en· W4392605535 on OpenAlexaffvenueabout
Helen Novak Lauscher, Chad Kim Sing, Chantz Strong, Anita Palepu, Jason Jaswal, Dietrich Fürstenburg, Nelly D. Oelke, Patricia Kay Pearce, Kendall Ho

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2024
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsCanadian Patient Safety InstituteOkanagan University CollegeUniversity of British Columbia, Okanagan CampusVancouver Coastal HealthPositive Living NorthOkanagan CollegeCanadian Medical Protective AssociationUniversity of Northern British ColumbiaUniversity of British Columbia
Fundersnot available
KeywordsEquity (law)WorkforceHealth equityDigital healthBusinessHealth careContext (archaeology)Public relationsPolitical sciencePublic economicsEconomic growthEconomicsGeography

Abstract

fetched live from OpenAlex

In this paper, we describe current pressures on health human resources (HHRs) in the Canadian context and related factors that impact equity-deserving communities/populations. We explore issues of HHR challenges in rural, remote and urban underserved contexts and explore the associated benefits and challenges of incorporating digital health (DH). We present examples and evidence of integrating hybrid models of care as a means of supporting HHRs via DH in the publicly funded health system.

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)0.031
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.131
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.071
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.026
Scholarly communication0.0160.034
Open science0.0040.024
Research integrity0.0150.017
Insufficient payload (model declined to judge)0.0510.005

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 head0.094
GPT teacher head0.424
Teacher spread0.330 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes3
Has abstractyes

Explore more

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