Intentions of Canadian healthcare professionals to prescribe exercise to people with amyotrophic lateral sclerosis
Bibliographic record
Abstract
Lack of effective treatment options exist for individuals with amyotrophic lateral sclerosis (ALS). A relatively inexpensive treatment option for people with ALS (PALS) is exercise. However, it is unclear whether healthcare professionals (HCP’s), working in ALS clinics across Canada, currently prescribe exercise to PALS. The aim of this study is to measure HCP’s intentions towards exercise for their patients with ALS. The theory of planned behaviour (TPB) was used to create and structure items in the survey. The web survey was sent to 17 ALS clinics in Canada. A total of 84 HCP’s completed the survey. We analyzed factors facilitating or hindering HCP’s to prescribe strength, aerobic and flexibility exercise to PALS. Results demonstrate that HCP’s are divided in their intentions to prescribe exercise to their patients with ALS. Perceived behavioural control (PBC) was the only TPB construct significantly related to the intention to prescribe all three exercise modes among physicians in the sample. For the non-physician HCP group, a significant correlation was found between the PBC construct and the intention to prescribe flexibility exercise (P < 0.01). Significant correlations in the non-physician group were also found between intentions to prescribe exercise for all three modes of exercise and: use, familiarity, and proportion of patients capable of exercising according to the ACSM guidelines and extent of team involvement present (P < 0.01). Qualitative themes revealed that the main reasons physicians do not prescribe exercise are related to: lack of confidence and competence (31% physicians), perceptions of lack of evidence supporting benefits of exercise in PALS (22%) and lack of time, space and resources to prescribe exercise to PALS (22%). The main reasons non-physician HCP’s did not prescribe exercise to their patients were related to: lack of confidence and competence (32% non-physician) and patient compliance and tolerance (30%). Our study suggests that a main deterrent among physicians are their perceptions regarding sufficient scientific evidence to reinforce the benefits of exercise prescription for PALS. Finding from our study also indicate that 55% of non-physician HCP’s believe prescribing exercise to PALS is outside their scope of practice. These results imply that different approaches may be required to increase exercise prescription intentions among different HCP specialities.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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) | 0.003 | 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".