A journey through roses and thorns: becoming a physician by learning from patients with life-threatening illnesses. A qualitative study with international medical students
Bibliographic record
Abstract
The medical students' well-being may be threatened by various stressors associated with providing care to different kinds of patients. This study aims to explore students' clinical experiences with patients who suffer from life-threatening illnesses, focusing on potential risk and protective factors. Audio-recorded and face-to-face interviews were conducted and transcribed verbatim. The "Interpretive Description" approach was used to analyse data. Overall, ten medical students with a mean age of 28 years old were interviewed. Well-being promoting factors were the following: therapeutic relationships, work-life balance, social support and communication, perception of improvement in knowledge and availability of advanced directives. Whilst factors that may reduce well-being included death exposure, managing emotions, communication difficulties, internal conflicts and disagreements, lack of knowledge and subjective concerns. These findings shed light on facets that are inherent parts of clinical experience with patients suffering from a life-threatening illness and that may turn in risk or protective factors for the medical students. Understanding the students' subjective experiences may aid in the improvement of the current educational programs, as well as in the development of tailored supportive and preventative interventions to promote well-being and professional competencies among this kind of students.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".