Co‐development and evaluation of the Musculoskeletal Telehealth Toolkit for physiotherapists
Bibliographic record
Abstract
INTRODUCTION: In-person physiotherapy services are not readily available to all individuals with musculoskeletal conditions, especially those in rural regions or with time-intensive responsibilities. The COVID-19 pandemic highlighted that telehealth may facilitate access to, and continuity of care, yet many physiotherapists lack telehealth confidence and training. This project co-developed and evaluated a web-based professional development toolkit supporting physiotherapists to provide telehealth services for musculoskeletal conditions. METHODS: A mixed-methods exploratory sequential design applied modified experience-based co-design methods (physiotherapists [n = 13], clinic administrators [n = 2], and people with musculoskeletal conditions [n = 7]) to develop an evidence-informed toolkit. Semi-structured workshops were conducted, recorded, transcribed, and thematically analysed, refining the toolkit prototype. Subsequently, the toolkit was promoted via webinars and social media. The usability of the toolkit was examined with pre-post surveys examining changes in confidence, knowledge, and perceived telehealth competence (19 statements modelled from the theoretical domains framework) between toolkit users (>30 min) and non-users (0 min) using chi-squared tests for independence. Website analytics were summarised. RESULTS: Twenty-two participants engaged in co-design workshops. Feedback led to the inclusion of more patient-facing resources, increased assessment-related visual content, streamlined toolkit organisation, and simplified, downloadable infographics. Three hundred and twenty-nine physiotherapists from 21 countries completed the baseline survey, with 172 (52%) completing the 3-month survey. Toolkit users had greater improvement in knowledge, confidence, and competence than non-users in 42% of statements. Seventy-two percentage of toolkit users said it changed their practice, and 95% would recommend the toolkit to colleagues. During the evaluation period, the toolkit received 5486 total views. DISCUSSION: The co-designed web-based Musculoskeletal Telehealth Toolkit is a professional development resource that may increase physiotherapist's confidence, knowledge, and competence in telehealth.
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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 | 0.044 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".