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Record W4205124796 · doi:10.34172/ijhpm.2022.6892

Articulating Concepts Matters! Resilient Actions in the Norwegian Governmental Response to the COVID-19 Pandemic Comment on "Government Actions and Their Relation to Resilience in Healthcare During the COVID-19 Pandemic in New South Wales, Australia and Ontario, Canada"

2022· letter· en· W4205124796 on OpenAlexaboutno aff
Sina Furnes Øyri, Siri Wiig

Bibliographic record

VenueInternational Journal of Health Policy and Management · 2022
Typeletter
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicHealth careResilience (materials science)Context (archaeology)ConceptualizationGovernment (linguistics)ScrutinyPsychological resilienceNorwegianHealthcare systemCoronavirus disease 2019 (COVID-19)Political scienceMedicinePsychologyComputer scienceGeographyDiseaseSocial psychology

Abstract

fetched live from OpenAlex

The coronavirus disease 2019 (COVID-19) pandemic has challenged our healthcare systems and required collaboration from both centralized and decentralized system levels to adapt to the changes and challenges. This commentary offers a look into the Norwegian governmental healthcare system and response within a resilience in healthcare perspective, by analyzing the situated, structural, and systemic resilience. Such a conceptualization of resilience into three scales of organizational activity may assist our efforts to understand and explain governmental actions throughout the pandemic. Research application of resilience in healthcare to explain and discuss government actions during the COVID-19 pandemic, needs to ensure sensitivity to the overall structural, cultural, and human factor aspects of the relevant healthcare system under scrutiny as well as sensitivity to specific context within the various system levels.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.160
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.131
GPT teacher head0.447
Teacher spread0.316 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations10
Published2022
Admission routes1
Has abstractyes

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