Bibliographic record
Abstract
He was a previously healthy middle-aged man who recently experienced his first emergency department visit, one he recounted may be his last. As I listened attentively at the end of the phone line, I cringed as he described in vivid detail his recent experience. He presented with sudden-onset back pain after a lifting injury at work. He asked to remain in a stretcher as his pain was less in the supine position. He was told to sit in a chair. He asked for Tylenol and was told to wait. He lied on the floor of the waiting room to reduce his pain; security was called as he was a “difficult” patient. His interaction with the healthcare team was brief; some over-the-counter analgesia, reassurance, and discharged home. His concerns were ignored. He was publicly embarrassed. We did not listen. In our follow-up call together, I actively listened and acknowledged his awful experience. His tone changed from anger to relief. He was genuinely appreciative for the opportunity to share his story, one that I will never forget. Actively listening to our patients is an essential tenet of our training; however, it is often easier said than done. We are very quick to cut patients off as they outline their concerns and often the result is that patients feel they are not heard. In the quiet calling space, I recognize and appreciate the verbal cues as I hear the joy, upset, frustration, and pleasure in their voices and imagine myself in their shoes. There are fewer distractions as I am not pulled in numerous directions in the oft-chaotic emergency department. I take the time to critically reflect on their experience and what it means to be a patient. For the patient, I have heard repeatedly that the follow-up calls offer an opportunity to be heard. It confirms and reaffirms to the patient that we care and are committed to the patient experience. Moreover, it offers the patient an opportunity to reflect on the emotion-filled experience of having to come to an emergency department to seek help. Patient satisfaction seems to increases when we listen, and listen well. The art of medicine, just like the science of medicine, is a learned craft. It takes practice and an unwavering commitment to the patient. In the hustle and bustle of the day-to-day experience, it can be lost easily. Additionally, my residency experience is largely focused on the science, rather than the art, of medicine. Trainees need regular and sustained opportunities to foster humanism, critical reflection, and active listening skills. Connecting with patients through brief follow-up phone calls is an effective way to hone one's active listening and reflection skills while simultaneously improving the patient experience.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.073 | 0.020 |
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".