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Record W2146355500 · doi:10.1186/1748-5908-8-s1-s9

The future of quality improvement research

2013· article· en· W2146355500 on OpenAlex
Rebecca S. Miltner, Jeremiah H Newsom, Brian S. Mittman

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueImplementation Science · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsnot available
Fundersnot available
KeywordsCLARITYGeneralizability theoryMedicineHealth services researchHealth careHealth administrationQuality managementQuality (philosophy)Health informaticsComparative effectiveness researchRelevance (law)Public healthNursingService (business)MarketingAlternative medicineBusinessPsychologyPolitical science

Abstract

fetched live from OpenAlex

Presentation The history of quality improvement research (QIR) demonstrates the large growth in improvement activities from early quality assessment and small area variation work through the adoption of industrial quality improvement methods in healthcare operations to the recent opportunities inherent in the Affordable Care Act of 2010. But after 40 years of development, significant growth in these scholarly activities has not produced comparable growth in insights, practical guidance, or progress toward better care. Five challenges influence the trajectory of improvement work and implementation science. The first challenge relates to the innovations and evidence base to improve healthcare. The focus of innovation and research in improvement has been on strategies, facilities and systems that are leaders in performance and quality improvement – the organizational equivalents of healthy white males. This makes differentiation and generalizability to the range of organizational settings difficult. A concerted effort must be made to focus on research conducted within, and with relevance to, the broader practice environment. The second challenge includes multiple logistical barriers such as access to study sites, the limited funding opportunities for QIR, and the lack of consistent IRB guidance and interpretation of regulations. Underlying these barriers is a considerable lack of clarity surrounding the nature of QIR relative to other types of health research. With the exception of the recent statement on cluster randomized trials by the Ottawa Ethics of Cluster Randomized Trials Consensus Group (2012)[1], the absence of consistent guidelines for QIR poses challenges for researchers trying to obtain local IRB review as well as investigators competing with others using more traditional research methods during the grant review process. QIR researchers need to embrace the IRB process and develop explicit, consensus-based guidance to facilitate more consistent reviews at the funding and IRB stages. The third challenge includes the professional differences that stem from the diversity of academic disciplines and types of institutions from which people involved in this work emerge. The concepts and definitions arising from diverse disciplinary roots make it difficult to achieve progress and move forward collectively. These factors pose barriers to the scholarly QIR community and, more importantly, contribute to confusion and decreased credibility among external stakeholders and scientists in the more traditional fields of study surrounding QIR. Lack of consensus and clarity impede the advance of this science because researchers cannot explain the work consistently to funding agencies, editorial review boards and other stakeholders. The fourth challenge is the need to strengthen the theoretical foundations for this work. There is an urgent need to assess whether we have the right theories, too many theories or simply a lack of guidance in using theories to build the science of improvement. The weak theoretical basis for QIR contributes to the fifth and final challenge to the future, which is advancing the science through robust and appropriate research approaches, designs and methods. The field has failed to reach consensus about the major research questions and goals for QIR, and continues to debate the appropriate methods for improvement and implementation work. Different views regarding the value and need for various research approaches and methods for conducting QIR limits the production of practical and effective insights and tools for researchers, clinicians, organizations, and policy decision makers.

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)0.039
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.868
GPT teacher head0.761
Teacher spread0.107 · 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