Contrasting Patients' and Healthcare Professionals' Experience in Hematological Cancer Care Pathway: A Narrative Study
Bibliographic record
Abstract
Hematological cancers represent 10% of cancers diagnosed in Canada. Treatments involve complex care pathways and various modalities as well as the management and monitoring of multiple side effects. There is limited understanding of these pathways from the perspectives of the people living with cancer (PLC) and the healthcare professional (HCP). The aim of this article is to contrast the experiences of PLCs and HCPs in the context of hematological cancer care pathway. Narrative study approach was chosen for this exploratory study. Twenty-one narratives were co-created with PLCs (n = 12) and HCPs (n = 9). A structural and comparative analysis of the narratives was performed. Results underline how PLCs' and HCPs' experiences were intertwined but not interdependent. PLCs seek to reconfigure their lives, while HCPs aim to individualize and enhance care for their patients. Furthermore, the shared experience between PLCs and HCPs proved beneficial for both groups. Ultimately, our findings underscore the need to access greater understanding into the dynamics of the relationship between PLCs and HCPs that could enhance quality of healthcare and services in the context of hematological cancer care.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".