Compassion training in healthcare: what are patients’ perspectives on training healthcare providers?
Bibliographic record
Abstract
BACKGROUND: The purpose of this qualitative study was to investigate advanced cancer patients' perspectives on the importance, feasibility, teaching methods, and issues associated with training healthcare providers in compassionate care. METHODS: This study utilized grounded theory, a qualitative research method, to develop an empirical understanding of compassion education rooted in direct patient reports. Audio-recorded semi-structured interviews were conducted to obtain an in-depth understanding of compassion training from the perspectives of hospitalized advanced cancer patients (n = 53). Data were analyzed in accordance with grounded theory to determine the key elements of the underlying theory. RESULTS: Three overarching categories and associated themes emerged from the data: compassion aptitude, cultivating compassion, and training methods. Participants spoke of compassion as an innate quality embedded in the character of learners prior to their healthcare training, which could be nurtured through experiential learning and reflective practices. Patients felt that the innate qualities that learners possessed at baseline were further fashioned by personal and practice experiences, and vocational motivators. Participants also provided recommendations for compassion training, including developing an interpersonal relationship with patients, seeing the patient as a person, and developing a human connection. Teaching methods that patients suggested in compassion training included patient-centered communication, self-reflection exercises, and compassionate role modeling. CONCLUSIONS: This study provides insight on compassion training for both current and future healthcare providers, from the perspectives of the end recipients of healthcare provider training - patients. Developing a theoretical base for patient centred, evidence-informed, compassion training is a crucial initial step toward the further development of this core healthcare competency.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".