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Record W4394367393 · doi:10.6084/m9.figshare.6080699

Examining physiotherapist use of structured aerobic exercise testing to decrease barriers to aerobic exercise

2018· dataset· en· W4394367393 on OpenAlexaboutno aff
Barbara Foster, Julia Fraser, Elizabeth L. Inness, Sarah Munce, Louis Biasin, Vivien Poon, Mark Bayley

Bibliographic record

VenueFigshare · 2018
Typedataset
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsAerobic exercisePhysical therapyPsychologyPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Objective: To determine the frequency of physiotherapist-administered aerobic exercise testing/training, the proportion of physiotherapists who administer this testing/training, and the barriers that currently exist across different practice environments. A secondary objective is to identify the learning needs of physiotherapists for the development of an education curriculum in aerobic exercise testing and training with electrocardiograph (ECG) administration and interpretation. Design: National, cross-sectional survey. Participants: Registered physiotherapists practicing in Canada. Results: Out of 137 participants, most (75%) physiotherapists prescribed aerobic exercise on a regular basis (weekly); however, 65% had never conducted an aerobic exercise test. There were no significant differences in frequency of aerobic exercise testing across different practice environments or across years of physiotherapy experience. Physiotherapists perceived the main barriers to aerobic exercise testing as being a lack of equipment/space (78%), time (65%), and knowledge (56%). Although most (82%) were uncomfortable administering 12-lead ECG-monitored aerobic exercise tests, 60% stated they would be interested in learning more about ECG interpretation. Conclusion: This study found that physiotherapists are regularly implementing aerobic exercise. This exercise was infrequently guided by formal aerobic exercise testing, which could increase access to safe and effective exercise within the optimal aerobic training zone. As well, this could facilitate training in patients with cardiovascular diagnoses that require additional testing for medical clearance. Increased ECG training and access to equipment for physiotherapists may augment pre-screening aerobic exercise testing. This training should include learning the key arrhythmias for aerobic exercise test termination as defined by the American College of Sports Medicine.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.083
GPT teacher head0.300
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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