Social Support Behaviours and Barriers in Group Online Exercise Classes for Adults Living with and beyond Cancer: A Qualitative Study
Bibliographic record
Abstract
Social support can be facilitated through exercise programs for people living with cancer, but there is limited research on how best to foster it in online exercise oncology classes. This study examined current training that fitness professionals receive on the provision and facilitation of social support, experiences people living with cancer have with social support, and supportive behaviours and barriers for providing and obtaining support in online group exercise oncology programs in Calgary, Alberta, Canada. Guided by interpretive description methodology, training materials were reviewed, observations of fitness professional training and online exercise classes (n = 10) were conducted, and adults living with and beyond cancer (n = 19) and fitness professionals (n = 15) were interviewed. These data were collected from January 2021 to June 2021. Analysis of the data collected resulted in the identification of three themes: Creating a welcoming environment, helping improve exercise ability and reach goals, and learning to provide and facilitate support online. A catalogue of supportive behaviours that can help to provide and facilitate and barriers that can hinder the provision and obtaining of social support in exercise oncology classes is presented. The findings provide guidance when structuring online classes and inform developing strategies for fitness professionals to use in online classes to foster social support by considering the wants and needs of participants, facilitating support between participants with similar experiences and interests, and integrating support into physical activity.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".