Learning to provide humanistic care and support in the context of chronic illness: Insights from the narratives of healthcare professionals in hemato-oncology
Bibliographic record
Abstract
PURPOSE: To document the process by which healthcare professionals (HCPs) support people living with and beyond hematological cancer and detail how they learned from their personal and clinical experience. METHOD: Using a narrative approach, we conducted nine semi-structured interviews with HCPs, including nurses, from a specialized care centre who support patients with hematological cancer. Interviews aimed to capture experiential learning gained from their practice. We performed a hybrid inductive/deductive content analysis on data using a framework based on sociological and educational models of experiential learning. RESULTS: Among healthcare professionals, analysis revealed the need to provide care and support that is 'humane' and adapted to each patient. Learning to provide this type of care proved to be challenging. Over the course of their clinical experience, healthcare professionals learned to adapt the support they provided by straddling a boundary between sympathy and empathy. Learning outcomes were associated with personal-professional development among participants. CONCLUSION: Our findings bring to light an overlooked facet of patient support in the context of cancer care, which is the acquisition of the soft skills required to deliver humanistic care and support. This learning process requires time and involves navigating between the realms of sympathy and empathy. Experiential learning is intertwined with the complexity of the often long-term patient-professional relationship that characterizes hemato-oncology. This unique relationship offers rewards for healthcare professionals on both personal and professional fronts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".