Factors That Affect Knowledge-Sharing Behaviors in Medical Imaging Departments in Cancer Centers: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Knowledge management plays a significant role in health care institutions. It consists of 4 processes: knowledge creation, knowledge capture, knowledge sharing, and knowledge application. The success of health care institutions relies on effective knowledge sharing among health care professionals, so the facilitators and barriers to knowledge sharing must be identified and understood. Medical imaging departments play a key role in cancer centers. Therefore, an understanding of the factors that affect knowledge sharing in medical imaging departments should be sought to increase patient outcomes and reduce medical errors. OBJECTIVE: The purpose of this systematic review was to identify the facilitators and barriers that affect knowledge-sharing behaviors in medical imaging departments and identify the differences between medical imaging departments in general hospitals and cancer centers. METHODS: We performed a systematic search in PubMed Central, EBSCOhost (CINAHL), Ovid MEDLINE, Ovid Embase, Elsevier (Scopus), ProQuest, and Clarivate (Web of Science) in December 2021. Relevant articles were identified by examining the titles and abstracts. In total, 2 reviewers independently screened the full texts of relevant papers according to the inclusion and exclusion criteria. We included qualitative, quantitative, and mixed methods studies that investigated the facilitators and barriers that affect knowledge sharing. We used the Mixed Methods Appraisal Tool to assess the quality of the included articles and narrative synthesis to report the results. RESULTS: A total of 49 articles were selected for the full in-depth analysis, and 38 (78%) studies were included in the final review, with 1 article added from other selected databases. There were 31 facilitators and 10 barriers identified that affected knowledge-sharing practices in medical imaging departments. These facilitators were divided according to their characteristics into 3 categories: individual, departmental, and technological facilitators. The barriers that hindered knowledge sharing were divided into 4 categories: financial, administrative, technological, and geographical barriers. CONCLUSIONS: This review highlighted the factors that influenced knowledge-sharing practices in medical imaging departments in cancer centers and general hospitals. In terms of the facilitators and barriers to knowledge sharing, this study shows that these are the same in medical imaging departments, whether in general hospitals or cancer centers. Our findings can be used as guidelines for medical imaging departments to support knowledge-sharing frameworks and enhance knowledge sharing by understanding the facilitators and barriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".