Absorptive capacity in the adoption of innovations in health: a scoping review
Bibliographic record
Abstract
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 INCLUSION CRITERIA: This scoping review included published and unpublished primary studies (ie, experimental, quasi-experimental, observational, and qualitative study designs), as well as reviews that broadly focused on the adoption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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.032 | 0.031 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".