Exploring Physical Activity Referral Barriers and Facilitators for Pediatric Oncology Healthcare Providers
Bibliographic record
Abstract
Purpose: This study explores why healthcare providers (HCPs) refer or do not refer to the Implementation of Physical Activity for Children and Adolescents on Treatment (IMPACT) trial, their barriers and facilitators for referral, and ways to improve the consenting process in clinical settings. Methods: Semi-structured interviews informed by the capability, opportunity, motivation- behaviour (COM-B) model were conducted with HCPs who were conveniently sampled from the pediatric oncology and hematology groups at the Alberta Children’s Hospital and Stollery Children’s Hospital. Interviews were recorded, transcribed verbatim and analysed using conventional content analysis to generate codes and categories. Categories were then deductively mapped to the COM-B domains. Results: Interviews (n=15) occurred at the HCPs’ convenience via phone (n=1), Zoom (n=13), or in person (n=1). Interview lengths ranged from 15.24 to 28.24 minutes, with an average of 19.00 minutes. Three categories emerged from the analysis: 1) Perceptions of Physical Activity (PA) and the IMPACT Trial, 2) HCP Roles Influence Referral to IMPACT and PA Discussions, and 3) Pathways to Participation: The IMPACT Referral and Consent Processes. Conclusion: These findings highlight key barriers and facilitators across HCPs capabilities, opportunities and motivations that influence their referral to IMPACT. Addressing HCP barriers through a streamlined referral system may enhance the referral and consent processes, supporting the implementation of IMPACT in Alberta. Strengthening referral pathways could help more children and adolescents affected by cancer access tailored PA interventions, such as IMPACT. Further research is needed to establish sustainable PA referral pathways by utilizing the role of qualified exercise professionals as a team member within pediatric oncology 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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".