LGBT-related educational contents and challenges in bioethics subjects in Japanese medical and nursing departments
Bibliographic record
Abstract
Background and objectives: In Japan, there are delays in the LGBT anti-discrimination law and ensuring human rights. In particular, inadequate response by medical field has been pointed out, and medical education is required not only from the health care needs but also from human rights. This study clarifies the educational contents and challenges related to LGBT in the bioethics subjects in the faculty of medicine and nursing and provides suggestions as one of diversity education.Methods: An anonymous self-administered survey was conducted on the educators in charge of bioethics subjects at 364 universities (81 medical schools and 283 nursing schools). Selective items were simply tabulated, and free descriptions were categorized and analyzed based on commonalities.Results: The response rate was 27.2% (faculty of medicine 29.7%, faculty of nursing 26.5%). “Incorporated,” (33.3%) “want to incorporate,” (21.2%) and “not incorporated” (45.4%) the content related to LGBT into the class. Many of the reasons for adopting it were “related to the way of life and dignity” and “need proper knowledge and understanding of those who identify as LGBT for health care providers.” The contents of education were mostly “definition of words,” “diversity of sexuality,” and “discrimination and prejudice.” Educational challenges included educational consideration for the parties concerned, securing of hours, coordination with other subjects, and educator’s ability.Conclusions: In bioethics subjects, it was suggested that the education is not limited to the improvement of clinical abilities, but that students consider the power structure and institutional inequality of society, notice their own preconceptions, and turn one's thoughts inward.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".