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Record W2756050684 · doi:10.12927/hcpol.2017.25193

An Integrated Needs-Based Approach to Health Service and Health Workforce Planning: Applications for Pandemic Influenza

2017· article· en· W2756050684 on OpenAlexafffundvenueabout
Gail Tomblin Murphy, Stephen Birch, Adrian MacKenzie, Janet Rigby, Joanne M. Langley

Bibliographic record

VenueHealthcare policy · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsHamilton Health SciencesDalhousie University
FundersCanadian Institutes of Health ResearchMinistry of Health, British ColumbiaNova Scotia Department of Health and WellnessDepartment of Health, Western Cape GovernmentNova Scotia Health Research Foundation
KeywordsWorkforceBusinessPandemicHealth careContext (archaeology)Variety (cybernetics)Workforce planningSurge CapacityService (business)Natural disasterNeeds assessmentCoronavirus disease 2019 (COVID-19)MedicineEconomic growthMarketingComputer sciencePolitical scienceGeographyEconomics

Abstract

fetched live from OpenAlex

Healthcare systems must be responsive to the healthcare needs of the populations they serve. However, typically neither health services nor health workforce planning account for populations' needs for care, resulting in substantial and unnecessary unmet needs. These are further exacerbated during unexpected surges in need, such as pandemics or natural disasters. To illustrate the potential of improved methods to help planning for these types of events, we applied an integrated, needs-based approach to health service and workforce planning in the context of a potential influenza pandemic at the provincial level in Canada. This application provides evidence on the province's capacity to respond to surges in need for healthcare and identifies specific services which may be in short supply in such scenarios. This type of approach can be implemented by planners to address a variety of health issues in different contexts.

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)0.009
metaresearch head score (Gemma)0.016
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 score0.107
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0030.003
Scholarly communication0.0050.003
Open science0.0030.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.307
GPT teacher head0.565
Teacher spread0.257 · 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

Citations32
Published2017
Admission routes4
Has abstractyes

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