Involving Physiotherapists in the Conduct of Research: A Mixed Methods Study of Physiotherapist Experiences, Perceptions, and Clinical Practice in a Research Project Using a Standardized Clinical Measure of Standing Balance
Bibliographic record
Abstract
Purpose: The objectives of this study were to understand the experiences, perceptions, and clinical practice of physiotherapists involved in planning, data collection, and interpretation for a study using a standardized measure of standing balance (the Mini Balance Evaluation Systems Test [Mini BESTest]). Method: We conducted a concurrent mixed methods study. We conducted semi-structured interviews with five phsyiotherapists exploring perceptions and experiences. We administered questionnaires on study satisfaction, confidence, and intention to use the Mini BESTest six times during the study. We extracted use of the Mini BESTest on non-study patients from a patient database. Results: Physiotherapists administered the Mini BESTest for all 59 clinical study patients. Study satisfaction was high (median 80%) and increased over time ( p < 0.05). Physiotherapists described generally positive experiences. Confidence in ability to administer, score, and interpret the Mini BESTest increased (all p < 0.05), although perceptions of the Mini BESTest varied. Intention to use the Mini BESTest did not change, and physiotherapists used the Mini BESTest on non-study patients on average 11 times during the clinical study (range, min-max, 0–33). Conclusions: Involving physiotherapists in clinical research increased confidence and use of the measure with non-study patients.
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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 | 0.042 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".