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Record W4290759908 · doi:10.11124/jbies-21-00436

Absorptive capacity in the adoption of innovations in health: a scoping review

2022· review· en· W4290759908 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.

Bibliographic record

VenueJBI Evidence Synthesis · 2022
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsHealth CanadaSt. Michael's HospitalQueen's University
FundersCanadian Institutes of Health Research
KeywordsAbsorptive capacityBusinessKnowledge managementComputer scienceIndustrial organization

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this scoping review was to explore how absorptive capacity has been conceptualized and measured in studies of innovation adoption in health care organizations. INTRODUCTION: Current literature highlights the need to incorporate knowledge translation processes at the organizational and system level to enhance the adoption of new knowledge into practice. Absorptive capacity is a set of routines and processes characterized by knowledge acquisition, assimilation, transformation, and application. A key concept in organizational learning theory, absorptive capacity is thought to be critical to the adoption of new knowledge and innovations in organizations. To understand how absorptive capacity was conceptualized and measured in health care organizations, it was appropriate to conduct a scoping review to answer our research question. INCLUSION CRITERIA: This scoping review included published and unpublished primary studies (ie, experimental, quasi-experimental, observational, and qualitative study designs), as well as reviews that broadly focused on the adoption of innovations at the organizational level in health care, and framed innovation adoption as processes that rely on organizational learning and absorptive or learning capacity. METHODS: Searches included electronic databases (ie, MEDLINE, Embase, PsycINFO, CINAHL, and Scopus) and gray literature, as well as reference scanning of relevant studies. Study abstracts and full texts were screened for eligibility by two independent reviewers. Data extraction of relevant studies was also done independently by two reviewers. All discrepancies were addressed through discussion or adjudicated by a third reviewer. Synthesis of the extracted data focused on descriptive frequencies and counts of the results. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). RESULTS: The search strategies identified a total of 7433 citations. Sixteen papers were identified for inclusion, including a set of two companion papers, and data were extracted from 15 studies. We synthesized the objectives of the included studies and identified that researchers focused on at least one of the following aspects: i) exploring pre-existing capacity that affects improvement and innovation in health care settings; ii) describing factors influencing the spread and sustainability of organizations; iii) identifying measures and testing the knowledge application process; and iv) providing construct clarity. No new definitions were identified within this review; instead existing definitions were refined to suit the local context of the health care organization in which they were used. CONCLUSIONS: Given the rapidly changing and evolving nature of health care, it is important to understand both current best practices and an organization's ability to acquire, assimilate, transform, and apply these practices to their specific organization. While much research has gone into developing ways to implement knowledge translation, understanding an organization's internal structures and framework for seeking out and implementing new evidence as it relates to absorptive capacity is still a relatively novel concept.

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)0.035
metaresearch head score (Gemma)0.048
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.401
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.006
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.762
GPT teacher head0.672
Teacher spread0.090 · 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