Use of Electrophysical Agents: Findings and Implications of a Survey of Practice in Metro Toronto
Bibliographic record
Abstract
Purposes: (1) To determine the availability, use and factors associated with the use of electrophysical agents (EPAs) in the management of patients with musculoskeletal impairments in physical therapy (PT) outpatient clinics in Metro Toronto and (2) to determine sources of EPA information used by physical therapists (PTs) and their perceived need for continuing EPA education. Method: Information was collected by a questionnaire distributed to all eligible PTs in the defined area. Data included the characteristics of PTs and clinics, availability and use of EPAs by PTs, treatments received by patients, sources and influence of EPA information, use of support personnel and PTs' past education and future needs. Results: The response rate was 54.1 per cent. The results were analyzed for 125 eligible PTs. On a typical day, treatment excluded EPAs for 29 per cent of patients. Treatment included one, two and three or more EPAs for 36, 26, and 9 per cent of patients, respectively. On average, PTs used EPAs for more than half of their patients: the most significant factors associated with EPA use were type of patient funding and number of patients treated per hour. Place of entry-level education and years since graduation were associated with the sources and perceived importance of EPA information that PTs used. More than half of the respondents indicated an interest in continuing EPA education. Conclusions: EPAs are commonly included in PT management of persons with musculoskeletal impairments. Entry-level and continuing education should promote use of EPAs based on scientific evidence.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".