Knowledge mobilization in childhood cancer: A scoping review and content analysis
Bibliographic record
Abstract
Objective This scoping review aimed to map the literature on knowledge mobilization (KMb), the reciprocal exchange of knowledge and expertise among key partners to bridge research and practice, in the childhood cancer community, identifying key needs, barriers, facilitators, and alignment with the knowledge-to-action (KTA) framework. Methods A comprehensive search was completed across Medline, Scopus, and ERIC. Data on study characteristics, KMb categories, barriers/facilitators, and KTA approaches were extracted and analyzed. Results A total of 2522 unique articles were identified, of which 77 met the inclusion criteria (1988–2024). Most studies were conducted in high-income countries ( n = 54, 70.1%), with 5.2% from lower-middle-income countries ( n = 4) and none from low-income countries. Collectively, 27,888 professionals and 50,786 patients were included in the studies. Four key categories resurfaced for KMb needs in this community: information and communication; medical management; training and education; and supports. Categories related to barriers and facilitators for effective KMb include information complexity and standardization; technical challenges to KMb; staff resistance and engagement; resource limitations; cultural and social factors; caregivers’ and parents’ values; and time constraints. While 76.6% of studies ( n = 59) proposed KMb interventions, fewer than 20% ( n = 14, 18.2%) offered evidence of sustaining knowledge use over time. Conclusion The state of the literature indicates that the childhood cancer community faces complex KMb needs and barriers. Future research and innovation should focus on patient and family engagement, address socioeconomic and cultural disparities, and explore how technology can enhance KMb, practice, and policy.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".