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Record W2165522200 · doi:10.1186/1748-5908-5-79

Conservation of resources theory and research use in health systems

2010· article· en· W2165522200 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueImplementation Science · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsDalhousie UniversityInstitute of Population and Public HealthBridgepoint Active HealthcareUniversity of ManitobaUniversity of OttawaManitoba Health
FundersCanadian Institutes of Health Research
KeywordsHealth services researchHealth administrationKnowledge translationContext (archaeology)Nursing researchGovernment (linguistics)MedicineResource (disambiguation)Knowledge managementHealth informaticsResource dependence theoryHealth policyManagement sciencePublic healthEnvironmental resource managementNursingManagementComputer scienceEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Health systems face challenges in using research evidence to improve policy and practice. These challenges are particularly evident in small and poorly resourced health systems, which are often in locations (in Canada and globally) with poorer health status. Although organizational resources have been acknowledged as important in understanding research use resource theories have not been a focus of knowledge translation (KT) research. What resources, broadly defined, are required for KT and how does their presence or absence influence research use?In this paper, we consider conservation of resources (COR) theory as a theoretical basis for understanding the capacity to use research evidence in health systems. Three components of COR theory are examined in the context of KT. First, resources are required for research uptake. Second, threat of resource loss fosters resistance to research use. Third, resources can be optimized, even in resource-challenged environments, to build capacity for KT. METHODS: A scan of the KT literature examined organizational resources needed for research use. A multiple case study approach examined the three components of COR theory outlined above. The multiple case study consisted of a document review and key informant interviews with research team members, including government decision-makers and health practitioners through a retrospective analysis of four previously conducted applied health research studies in a resource-challenged region. RESULTS: The literature scan identified organizational resources that influence research use. The multiple case study supported these findings, contributed to the development of a taxonomy of organizational resources, and revealed how fears concerning resource loss can affect research use. Some resources were found to compensate for other resource deficits. Resource needs differed at various stages in the research use process. CONCLUSIONS: COR theory contributes to understanding the role of resources in research use, resistance to research use, and potential strategies to enhance research use. Resources (and a lack of them) may account for the observed disparities in research uptake across health systems. This paper offers a theoretical foundation to guide further examination of the COR-KT ideas and necessary supports for research use in resource-challenged environments.

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)0.062
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0620.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.844
GPT teacher head0.787
Teacher spread0.057 · 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