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Record W2110420621 · doi:10.1186/1478-4505-12-50

Advancing the application of systems thinking in health

2014· editorial· en· W2110420621 on OpenAlexfundno aff
Taghreed Adam

Bibliographic record

VenueHealth Research Policy and Systems · 2014
Typeeditorial
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
FundersAlliance for Health Policy and Systems ResearchInternational Development Research CentreWorld Health Organization
KeywordsHealth services researchContext (archaeology)Systems thinkingHealth policyHealth administrationComputer scienceData scienceManagement sciencePublic relationsPublic healthMedicineNursingArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

s, journal names and corresponding author’s affiliation. Number of publications mentioning these search terms was 1386 as of 14 August 2014. Adam Health Research Policy and Systems 2014, 12:50 Page 2 of 5 http://www.health-policy-systems.com/content/12/1/50 The applicability of a wide range of tools and approaches This Series illustrates how research approaches that are commonly used in various disciplines, such as realist evaluation, sense-making (as a mental model), or program evaluation theories, can be applied within a systems thinking approach to address complex health systems questions. It does so by showing that the types of questions asked are the most important element that shape the orientation of the analysis, not the tool itself. For example, in the paper by Prashanth et al. [7], systems thinking and complex adaptive systems approaches added depth to the realist evaluation by digging deeper into the drivers of, and the context in which the differences in responses of health workers in the two subdistricts were observed and what triggered them. They could show that settings with committed staff and positive intentions to make changes demonstrated more Figure 2 World map of the 1,386 MEDLINE records mentioning the te “systems science”. Source: GoPubMed, which reports the frequency that t abstracts, journal names and corresponding author’s affiliation. This data w positive outcomes and an ability to use existing opportunities to solve problems and improve performance. Further, that commitment alone was neither crucial nor sufficient as demonstrated by findings from another setting with committed staff but different outcomes. Finally, that in settings with a lack of commitment from staff, strong leadership became more pronounced in driving the change into better outcomes [7].s, journal names and corresponding author’s affiliation. This data w positive outcomes and an ability to use existing opportunities to solve problems and improve performance. Further, that commitment alone was neither crucial nor sufficient as demonstrated by findings from another setting with committed staff but different outcomes. Finally, that in settings with a lack of commitment from staff, strong leadership became more pronounced in driving the change into better outcomes [7]. Systems thinking and mixed methods As discussed by Peters and demonstrated by several of the Series papers, both qualitative and quantitative methods contribute in their own way to our understanding of complexity [6,8]. As some of the early systems thinking literature originated from quantitative disciplines such as physics and biology, it may give the impression that relevant systems thinking approaches are rms “systems thinking”, “complex adaptive systems”, or erms appear in MEDLINE indexes for publications, which include titles, as obtained on 14 August 2014. Adam Health Research Policy and Systems 2014, 12:50 Page 3 of 5 http://www.health-policy-systems.com/content/12/1/50 predominantly quantitative. Perhaps one of the main contributions of this Series is demonstrating how qualitative methods commonly used in fields such as social science or anthropology add equally important value and depth to analyses of complex health systems questions and phenomena [8-12]. For example, they are often used to provide a profound initial understanding of the problem that can then be complemented by quantitative approaches that incorporate the learning into a more realistic and sophisticated quantitative analysis [6]. Exploiting the potential of visual interpretations of

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
gemmano category
Domain: not available · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: no
Not applicablemedium
gptno category
Domain: not available · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.061
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.322

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0610.048
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0130.007
Science and technology studies0.0030.030
Scholarly communication0.0190.019
Open science0.0030.013
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0080.001

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 head0.524
GPT teacher head0.579
Teacher spread0.055 · 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.

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

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

Citations108
Published2014
Admission routes1
Has abstractyes

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