From innovation to practice: factors influencing implementation of the PAROLE-Onco peer support program
Bibliographic record
Abstract
BACKGROUND: The PAROLE-Onco program, implemented in Quebec, integrates peer support (accompanying patients (APs)) into clinical oncology teams, to support cancer patients during their health trajectory. Despite the growing evidence of its benefits, scaling up this program across various healthcare settings presents significant challenges. The aim of the study is to explore healthcare professionals/managers' perceptions of the factors influencing the implementation and expansion of the PAROLE-Onco program. METHODS: A multiple-case case study approach was employed, collecting data from various stakeholders, including healthcare professionals, managers, and research team members, through semi-structured interviews and focus groups. The Consolidated Framework for Implementation Research (CFIR) and the Practice Change Model (PCM) were used to analyze barriers and facilitators to program implementation. RESULTS: A total of 26 healthcare professionals/managers responded to the online survey, providing insights into the challenges and opportunities related to the integration of APs. The study identified key external, organizational, and individual factors affecting implementation. Outer setting factors, such as a supportive policy environment and sustainable funding, were essential but not sufficient on their own. Organizational challenges, including limited space, siloed structures, and the need for a strong culture of patient partnership, were significant barriers. At the individual level, factors like leadership commitment, openness to the program, and staff knowledge of the AP's role were crucial to successful implementation. Common barriers included time constraints, work overload, and resistance to change, while facilitators were engaged leadership, communication strategies, and role clarity for APs. CONCLUSIONS: The findings highlight the importance of multi-level factors, including leadership support, adequate resources, and staff engagement, in scaling up the PAROLE-Onco program. Successful implementation requires a tailored approach that considers the unique challenges of each healthcare setting. These insights can guide future efforts to optimize peer support programs in oncology and other healthcare areas, aiming for better patient care and outcomes.
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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 | 0.026 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".