Current Practice, Barriers to, and Facilitators of Exercise Testing and Training by Physiotherapists in Cystic Fibrosis Specialized Centres in Canada
Bibliographic record
Abstract
Purpose: This study surveyed physiotherapists working at Canadian cystic fibrosis (CF) specialized centres to investigate the current practice, barriers to, and facilitators of exercise testing and training. Method: Physiotherapists were recruited from 42 Canadian CF centres. They responded to an e-questionnaire regarding their practice. The data were analyzed using descriptive statistics. Results: Eighteen physiotherapists responded (estimated response rate of 23%); median years of clinical experience was 15 (range, min-max, 3–30) years. Aerobic testing was administered by 44% of respondents, strength testing by 39%, aerobic training by 78%, and strength training by 67%. The most frequently reported barriers across all four types of exercise testing and training were insufficient funding (reported by 56%–67% of respondents), time (50%–61%) and staff availability (56%). More late career than early career physiotherapists reported utilizing aerobic testing (50% vs. 33% of respondents), strength testing (75% vs. 33%), aerobic training (100% vs. 67%), and strength training (100% vs. 33%). Conclusions: Exercise testing and training is underutilized in Canadian CF centres. Experienced physiotherapists reported utilizing exercise testing and training more than less-experienced physiotherapists. Post-graduate education and mentorship, especially for less-experienced clinicians, are recommended to emphasize the importance of exercise testing and training. Barriers of funding, time, and staff availability should be addressed to further improve quality of 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".