Virtual or In-Person: A Mixed Methods Survey to Determine Exercise Programming Preferences during COVID-19
Bibliographic record
Abstract
A survey was conducted to identify barriers and facilitators to engaging in virtual and in-person cancer-specific exercise during COVID-19. A theory-informed, multi-method, cross-sectional survey was electronically distributed to 192 individuals with cancer investigating preferences towards exercise programming during COVID-19. Respondents had previously participated in an exercise program and comprised two groups: those who had experience with virtual exercise programming (‘Virtual’) and those who had only taken part in in-person exercise (‘In-Person’). Quantitative data were summarized descriptively. Qualitative data were thematically categorized using framework analysis and findings were mapped to an implementation model. The survey completion response rate was 66% (N = 127). All respondents identified barriers to attending in-person exercise programming during COVID-19 with concerns over the increased risk of viral exposure. Virtual respondents (n = 39) reported: (1) feeling confident in engaging in virtual exercise; and (2) enhanced motivation, accessibility and effectiveness as facilitators to virtual exercise. In-Person respondents (n = 88) identified: (1) technology as a barrier to virtual exercise; and (2) low motivation, accessibility and exercise effectiveness as barriers towards virtual exercise. Sixty-six percent (n = 58) of In-Person respondents reported that technology support would increase their willingness to exercise virtually. With appropriately targeted support, perceived barriers to accessing virtual exercise—including motivation, accessibility and effectiveness—may become facilitators. The availability of technology support may increase the engagement of individuals with cancer towards virtual exercise programming.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".