Absorptive capacity in the adoption of innovations in health care: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: 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. Absorptive capacity, a key concept in organizational learning theory, is thought to be critical to the adoption of new knowledge and innovations in organizations. INCLUSION CRITERIA: This scoping review will include primary studies (ie, experimental, quasi-experimental, observational, and qualitative study designs) and gray literature that broadly focus on the adoption of innovations at the organizational level in health care, and frame innovation adoption as processes that rely on organizational learning and absorptive or learning capacity. METHODS: Data sources will include comprehensive searches of electronic databases (eg, MEDLINE, Embase, PsycINFO, CINAHL, and Scopus), gray literature, and reference scanning of relevant studies. Study abstracts and full texts will be screened for eligibility by two reviewers, independently. Data extraction of relevant studies will also be done independently by two reviewers. All discrepancies will be addressed through further discussion or adjudicated by a third reviewer. Synthesis of the extracted data will focus on descriptive frequencies, counts, and thematic analysis and the results will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR).
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 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 | 0.145 | 0.120 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.029 | 0.020 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.053 | 0.014 |
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".