Minding the gaps: assessing and addressing clinical research core competencies across a network of Canadian cancer centres
Bibliographic record
Abstract
The Canadian Cancer Clinical Trials Network (3CTN, the Network), established in 2014 to address the decline in academic cancer clinical trials' (ACCT) activity, has successfully achieved incremental year-over-year accrual targets as well as implemented recognized performance measures and supports for improving efficiency and quality of trial activities at member sites across Canada. As part of efforts to address ongoing challenges of staff recruitment, retention, and turnover in academic institutions that have been more recently exacerbated by the pandemic, the Network's Performance Strategy Sub-Committee (PSC) oversaw surveys of site clinical research professionals intended to capture workforce development status and identify knowledge gaps using the Joint Task Force Core Competency Framework (JTF CCF) as the standard basis for assessment. Accountable to the 3CTN Management Committee, the PSC consists of clinical research operations experts across Canada responsible for overseeing implementation and monitoring progress of this initiative. Staff at 3CTN's adult sites evaluated and reported trial personnel core competencies and gaps according to each domain/leveled competency statement of the framework. The most frequently noted competency gaps were in the domains of: Investigational Product Development and Regulation (28%); Scientific Concepts and Research Design (16%); and Study and Site Management (14%). Reported data was compiled and represented in the 3CTN Core Competency Report, developed as a web-based, interactive tool enabling members and stakeholders to filter data to enumerate and quantify workforce competency gaps at their site, within their node of affiliated sites, or across the national Network. Concurrently, an environmental scan and review of education resources was conducted and reviewed by the PSC. Embedded links to curated learning and development resources were incorporated into the report and associated with each domain/leveled competency statement to provide ready access to high-quality learning and development resources where needed. In the remaining years of its current strategic plan, 3CTN will continue to monitor, develop collaborative initiatives to target prioritized clinical research competency gaps and create opportunities for ongoing assessment and reporting by sites to capture changes in workforce core competencies over time.
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 | 0.048 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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 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".