Bibliographic record
Abstract
Soon after we were married, the time came for the normalization of my wife's immigration status. She was from Ecuador, and had a visitor's visa. By the time I got my act together enough to start working on her immigration papers, we were already a few months' pregnant. I had just begun my second year of medical school. As part of normalization, my wife needed an “immigration physical,” consisting of blood tests and vaccines that should be administered by “civil surgeons.” These exams are typically expensive, so we traveled far to the office of Dr. Delphin. His rates were reasonable, but his character proved to be exceptional. After a brief wait in his office, we entered the exam room. Dr. Delphin greeted us warmly, and he proceeded to get to know us. He wanted to know who we were, what we did, why we were there. Somewhere in the conversation, he learned that I was in my second year of medical school, and I expressed to him how I wanted to have a similar practice when I finished school. He told me that he would like to help and invited me to come back to his office the following Saturday, to help and learn. The next week flew by, and I became busy with schoolwork. That Saturday, I forgot to go to his office. I would not have remembered it at all if it weren't for a phone call I got that night. “José,” Dr. Delphin said, “I was waiting for you all day.” I felt horrible, apologized profusely, and asked for a new appointment. He gave me a second chance. Since the beginning Dr. Delphin was invested in my success. At our next meeting, Dr. Delphin taught me to draw blood. With his patients, I learned how to take blood efficiently. I went to his office regularly for the rest of medical school. Over time we became friends and our families got to know each other. We spent holidays together. Dr. Delphin became a trusted mentor. As I reflect back on this experience, I recognize my good fortune in finding a friend and mentor in Dr. Delphin. Those hours I spent in his office were filled with advice and wisdom, and it was a place to establish clinical relevance for the much-hated book work of my second year. He taught me to respect patients, that time was worth more than money, and that my time was the greatest gift that I could give my patients. When one of his patients unexpectedly died, he spent hours with the family, in his role as physician. He taught me to be one with the patients, and that each patient was a gift. Our relationship never would have developed if it weren't for Dr. Delphin's interest in me. I was an overwhelmed medical student, and he was a successful practicing physician. I needed a mentor, but I did not know it. He sensed my need and became my mentor, using techniques that he knew would engage me. His influence lives in me today. My choice of career in family medicine is due to his example. My commitment to the underserved is a direct result of his teachings. My work as full-time academic physician has roots in our relationship. I now have countless opportunities to mentor medical students. But more than anything, my efforts to become a good mentor are to repay him. He taught me true mentoring, where the mentor expects nothing in return. From him I learned that the best mentoring relationships are those that develop on their own—and for that I give profound thanks. José E. Rodriguez, MD Ronald Delphin,MD
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.144 | 0.072 |
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".