Collaboration networks of the implementation science centers for cancer control: a social network analysis
Bibliographic record
Abstract
Abstract Background Multi-center research initiatives offer opportunities to develop and strengthen connections among researchers. These initiatives often have goals of increased scientific collaboration which can be examined using social network analysis. Methods The National Cancer Institute (NCI)-funded Implementation Science Centers in Cancer Control (ISC 3 ) initiative conducted an online social network survey in its first year of funding (2020) to (1) establish baseline network measures including the extent of cross-center collaboration and (2) assess factors associated with a network member’s access to the network such as one’s implementation science (IS) expertise. Members of the seven funded centers and NCI program staff identified collaborations in planning/conducting research , capacity building , product development , scientific dissemination , and practice/policy dissemination . Results Of the 192 invitees, 182 network members completed the survey (95%). The most prevalent roles were faculty (60%) and research staff (24%). Almost one-quarter (23%) of members reported advanced expertise in IS, 42% intermediate, and 35% beginner. Most members were female (69%) and white (79%). One-third (33%) of collaboration ties were among members from different centers. Across all collaboration activities, the network had a density of 14%, suggesting moderate cohesion. Degree centralization (0.33) and betweenness centralization (0.07) measures suggest a fairly dispersed network (no single or few central member(s) holding all connections). The most prevalent and densely connected collaboration was in planning/conducting research (1470 ties; 8% density). Practice/policy dissemination had the fewest collaboration, lowest density (284 ties’ 3% density), and the largest number of non-connected members ( n =43). Access to the ISC 3 network varied significantly depending on members’ level of IS expertise, role within the network, and racial/ethnic background. Across all collaboration activities, most connected members included those with advanced IS expertise, faculty and NCI staff, and Hispanic or Latino and white members. Conclusions Results establish a baseline for assessing the growth of cross-center collaborations, highlighting specific areas in need of particular growth in network collaborations such as increasing engagement of racial and ethnic minorities and trainees or those with less expertise in IS.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".