Exploring the Evidence for Occupational Therapists' Interventions with Clients with Lateral Epicondylitis
Bibliographic record
Abstract
Lateral epicondylitis is known to be a major problem in work absenteeism and permanent partial disability. Although occupational therapists are often asked to treat clients with this disorder, its management remains controversial since there is insufficient research evidence to favour any particular intervention. This study gathered information on the current interventions used by occupational therapists with clients with lateral epicondylitis according to three phases: acute, subacute and chronic. A self-administered questionnaire was sent to a convenience sample of 219 occupational therapists working in the province of Québec in Canada. After two reminders, a participation rate of 81% was obtained. Over 20 interventions were identified and sorted into five categories: education, activities/exercises, assistive devices, environment and pain management modalities. Overall, education about risk factors was the most frequently used intervention by occupational therapists in all phases. Activities/exercises formed the second most frequently used intervention group, but were more preferred in the subacute and chronic phases. Although many interventions are currently used in health care facilities, few studies on their effectiveness have been conducted. This study explored the gap between research and practice by surveying clinicians on their management of clients with lateral epicondylitis, allowing the targeting of areas considered useful for future studies in occupational therapy.
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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 | 0.025 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".