Thinking Globally in the Pursuit of Individual Identity: Diversity, Equity, and Inclusion in the International Journal of Medical Students (IJMS)
Bibliographic record
Abstract
Diversity, equity, and inclusion (DEI) are core values that are unequivocally essential to healthcare research and practice. However, global health inequities remain pervasive and disruptive to the delivery of healthcare. This unacceptable lack of inclusivity and equity infiltrates all aspects of medicine, including research and publication. Accordingly, there is a dissemination of unbalanced and homogenous perspectives which are not representative of the global population. The International Journal of Medical Students (IJMS) has strived to counter such biases through the development of content and the process of its publication. Further, the selection of its editorial team and ambassadors is conducted with the intention of diversity. We respect individual differences and celebrate them as strengths adding to the quality of our journal. Therefore, the IJMS has taken a positive step toward an equitable environment by publishing a policy statement on DEI. We hope to lead by example by fostering a culture of inclusivity for all researchers, regardless of background. Though, we recognize the complexity of implementing comprehensive DEI practices and consider it our duty to the community that we continuously develop through a dedicated effort and iterative process.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | MetaresearchScholarly communicationOpen science Domain: Evaluation · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.040 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.032 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".