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Record W2946928234 · doi:10.1186/s13012-019-0895-1

How do researchers conceptualize and plan for the sustainability of their NIH R01 implementation projects?

2019· article· en· W2946928234 on OpenAlexaff
Alekhya Mascarenhas Johnson, Julia E. Moore, David Chambers, Jennifer Rup, Camellia Dinyarian, Sharon E. Straus

Bibliographic record

VenueImplementation Science · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsSustainabilityOperationalizationPsychological interventionQualitative researchMedical educationSustainability organizationsMedicinePublic relationsProcess managementPolitical scienceBusinessNursingSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Inadequate sustainability of implementation of evidence-based interventions has led to calls for research on how sustainability can be optimized. To advance our understanding of intervention sustainability, we explored how implementation researchers conceptualized and planned for the sustainability of their implemented interventions with studies funded by the United States (US) National Institutes of Health (NIH). METHODS: We used sequential, mixed methods to explore how researchers conceptualized and planned for the sustainability of the health interventions using (1) a document review of all active and completed US NIH R01 Grants and Equivalents reviewed within the Dissemination and Implementation Research in Health (DIRH) Study Section between 2004 and 2016 and (2) a qualitative content analysis of semi-structured interviews with NIH R01 DIRH grant recipients. RESULTS: We found 277 R01 profiles within the DIRH study section listed on the US NIH RePORTER website including 84 that were eligible for screening. Of the 84 unique projects, 76 (90.5%) had primary implementation outcomes. Of the 76 implementation project profiles, 51 (67.1%) made references to sustainability and none referred to sustainability planning. In both profiles and interviews, researchers conceptualized sustainability primarily as the continued delivery of interventions, programs, or implementation strategies. Few researchers referenced frameworks with sustainability constructs and offered limited information on how they operationalized frameworks. Researchers described broad categories of approaches and strategies to promote sustainability and key factors that may influence researchers to plan for sustainability, such as personal beliefs, self-efficacy, perception of their role, and the challenges of the grant funding system. CONCLUSIONS: We explored how US NIH R01 DIRH grant recipients conceptualized and planned for the sustainability of their interventions. Our results identified the need to test, consolidate, and provide guidance on how to operationalize sustainability frameworks, and to develop strategies on how funders and researchers can advance sustainability research.

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 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)0.470
metaresearch head score (Gemma)0.579
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.530
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4700.579
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.008
Science and technology studies0.0080.032
Scholarly communication0.0240.035
Open science0.0080.016
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0020.001

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 head0.758
GPT teacher head0.734
Teacher spread0.024 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
GenreEmpirical

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

Citations53
Published2019
Admission routes1
Has abstractyes

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