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Record W2939588386 · doi:10.1177/0840470419830105

Harnessing instability as an opportunity for health system strengthening: A review of health system resilience

2019· review· en· W2939588386 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealthcare Management Forum · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsResilience (materials science)Healthcare systemProcess (computing)SustainabilityVariety (cybernetics)Psychological resilienceOrder (exchange)BusinessRisk analysis (engineering)Environmental resource managementHealth careComputer sciencePsychologyEconomic growthEconomicsSocial psychology

Abstract

fetched live from OpenAlex

In recent years, resilience has emerged as a prominent topic in global health systems discourse as a result of the increasing variety and volume of sources of instability inflicting strain on systems. In line with this study's intent to bring together existing literature on health system resilience as a means to understand the process through which systems achieve resilience, a review of academic literature related to health system resilience was conducted. Emerging from this review is an operational model of resilience that builds on existing health systems frameworks. The model highlights health system resilience as a process through which leaders in all sectors need to be mobilized in order to harness instability as an opportunity for health system strengthening rather than a threat to the system's sustainability and integrity.

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.

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.650
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.193
GPT teacher head0.428
Teacher spread0.235 · 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