Adaptive challenges of curriculum implementation for enhancing medical student resilience at Showa University in Japan
Bibliographic record
Abstract
It has been consistently reported that medical students experience a high rate of psychological morbidity, depersonalization, and low personal accomplishment around the world. Under the circumstances, resilience-enhancing programs have been gathering attention and partially implemented even in Japan. However, most of the programs just imitate resiliency programs in North America even though studies have indicated that there are cultural differences between East Asia and North America in the capacity to cope with a stressful situation. The presenters investigated what factors might affect the similarities or differences in the perception of resilience among experienced palliative care physicians in Canada and Japan in 2017-2018. This study showed that Japanese physicians are more likely to rely on “Relationships” with other persons such as family members, friends, mentors or colleagues; in contrast, Canadian physicians tended to be more focused on individual factors such as “Autonomy” and “Confidence”. As a result, the presenters at Showa University School of Medicine in Japan have implemented a progressively advancing resiliency program in a passed manner for the 1st through 6th year medical students as part of a new curriculum. This represents one of the most drastic revisions of curriculum in the school’s history. This presentation will introduce a course for resiliency programs as part of a new curriculum, including course description, course content, educational objectives, instructional strategies and the tips for the classroom teaching and learning.
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 | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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, 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".