Role, Resources, and Integration of Accompanying Patients in Oncology: A Qualitative Study from the Accompanying Patient’s Perspective
Bibliographic record
Abstract
BACKGROUND: In response to the growing emotional support needs of patients in oncology, peer support was introduced into clinical teams in Quebec, Canada, in 2018. These peers, called accompanying patients (APs), are former cancer patients who use their experiential knowledge to provide support to patients during their oncology journey. This paper aims to identify APs' perceptions of the program by including a diversity of perspectives. We include inexperienced and experienced APs, APs in different facilities, and APs in different cancer programs. METHODS: We conducted a qualitative study based on 12 semi-structured interviews of APs between June and August 2024 in Quebec, Canada. We explored four themes, building on the Practice Change Model for qualitative analysis: APs' sources of motivation, influences and environmental factors, resources available for AP integration, and the program's effects. RESULTS: 12 APs from 5 facilities participated in an interview. All the APs, both experienced and inexperienced, were highly motivated to participate in the program. Their motivations included a desire to give back to society, to help people and to give meaning to their illness. Both experienced and inexperienced APs were confident in their ability to accompany others. They were aware of their responsibilities and its limits regarding their role as an AP. They pointed out the program's positive impact on their own emotional well-being and that of the patients. The program also benefited the clinical team, by limiting unhelpful demands from patients and saving time for clinicians. However, experienced APs did not feel well integrated into the healthcare team. CONCLUSIONS: We concluded that APs are highly motivated to be in the program. They perceived a need for the program in the current health system. They noted its beneficial effects on patients, on themselves, and on the clinical team. However, more resources need to be directed toward integrating APs into healthcare teams.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".