Integrating Structured Peer Support Pathways for Patients Undergoing Cellular Therapies: Insights from a Participatory Study
Bibliographic record
Abstract
Individuals diagnosed with hematological cancer often face an emotionally demanding journey, frequently reporting high levels of distress and unmet needs, including a lack of personalized and emotional support. Our research team co-developed structured support provided by trained Accompanying patients (APs) during key stages of the care pathway for individuals with hematological cancers treated with cellular therapies. This paper presents findings on the anticipated needs that APs can address, their role within the care team, and the key facilitators, challenges, and strategies needed to implement structured peer support. Using a participatory action research approach, three working sessions were conducted with 16 key stakeholders to co-develop the structured support pathways. Data from group discussions (n = 3) underwent thematic analysis to identify essential components for adapting the support offered by APs, as well as the barriers and facilitators to its implementation. Findings show that APs can help address a wide range of practical, emotional, social, and psychological needs while enhancing patient understanding and continuity of care. Participants emphasized the importance of clear role definitions, adequate training, and supportive tools to ensure the safe and effective involvement of APs. Organizational and communication challenges were also identified, along with strategies to promote long-term sustainability. This initiative represents the first structured peer support program tailored to cellular therapies in Canada and offers a promising model to improve patient experience, foster relational continuity, and support cancer survivors across the care continuum.
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".