Increasing Diversity in Canadian Radiology: From the Hiring Process to Needed Active Retention Efforts
Bibliographic record
Abstract
The Canadian Association of Radiologists supports equity, diversity, and inclusion (EDI) in employment. It is imperative that institutions implement recruitment and retention practices to ensure a diverse workforce. This requires considerable attention to each step in the process, including the job posting, candidate search, hiring committee composition, interviews, hiring decision, and retention and promotion. Job postings must be widely distributed and visible to underrepresented groups. The candidate search should be completed by a diverse committee with expertise in EDI. All committee members must complete EDI and anti-bias training and conduct a broad search that ensures underrepresented groups are encouraged to apply. Interviews must be offered to all candidates. The hiring decision must avoid the use of subjective criteria. Recruitment of members of underrepresented groups ensures a diverse workforce, and organizations should commit resources to the retention and promotion of these members. Mentorship programs must be implemented and incentives provided to faculty members to serve as mentors. Transparent guidelines for promotion made universally available on department or institution websites. Recruiting a diverse workforce in Medical Imaging will only be achieved if EDI are central to the organization's goals and strategic plan. All organizational policies, practices, and procedures must be reviewed with an intersectional lens to identify potential gaps, areas for improvement, and areas of strength in the recruitment and retention of members of underrepresented groups.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".