How Can We Engage Oncology Care Providers and Glioblastoma Patients in Conversations About Physical Activity: A Qualitative Descriptive Study Using the Theoretical Domains Framework
Bibliographic record
Abstract
Glioblastoma (GB) is the most common primary malignant brain tumour in adults. Physical activity (PA) has value as a supportive service for individuals living with a GB diagnosis to help maintain quality of life and physical functioning. The objective of this study is to understand how oncology care providers (OCPs), family/friend caregivers, and health system decision makers can include conversations of PA into care for those living with a GB. We conducted 19 semi-structured interviews guided by the Capability, Opportunity, Motivation-Behaviour (COM-B) model and further refined them by the theoretical domains framework (TDF). The data were then analyzed using a directed content analysis using a codebook generated using the TDF. Patients and family/friend caregivers appreciated hearing about PA from their OCPs, from initial diagnosis into follow-up appointments, and they saw PA as a way to take a break from cancer/medically focused care, and historical PA behaviours did not mean patients were more or less likely to be open about PA discussions. This study further emphasises the inclusion of PA discussions in clinical care. OCPs in GB care feel they have the knowledge to partake in PA conversations, and GB patients are open to having these conversations. However, specific barriers are in place that do not lead to widespread implementation of PA discussions for all patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| 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".