Experiences of telehealth e-mentoring within postgraduate musculoskeletal physical therapy education in the UK and Canada: a protocol for parallel mixed-methods studies and cross-cultural comparison
Bibliographic record
Abstract
INTRODUCTION: Mentored clinical practice is central to demonstrating achievement of International Educational Standards in advanced musculoskeletal physical therapy. While traditionally delivered face-to-face, telehealth e-mentoring is a novel alternative to offering this unique pedagogy to facilitate mentee critical reflection, deeper learning and enhanced knowledge translation to optimise patient care. With COVID-19 resulting in widespread adoption of telehealth and access to mentors often limited by geography or cost, the potential value of telehealth e-mentoring needs investigating. To investigate the experiences and outcomes of multiple stakeholders (student mentees, mentors and patients) engaged in musculoskeletal physical therapy telehealth e-mentoring across two universities (UK and Canada). METHODS AND ANALYSIS: Using case study design, we will use sequential mixed methods involving qualitative and quantitative components based on existing evidence. To examine the influence of telehealth e-mentoring on health outcomes in patients with musculoskeletal complaints, we will use patient-reported outcomes for satisfaction, patient empowerment and change in musculoskeletal health. We will conduct semistructured interviews to explore the development of critical thinking, clinical reasoning, communication skills and confidence of students engaged in telehealth e-mentoring. To explore the mentor acceptability and appropriateness of telehealth e-mentoring, we will conduct a focus group in each site. Finally, we will include a focus group of participants from each site to allow a cross-cultural comparison of findings to inform international stakeholders. Quantitative data will be analysed using descriptive statistics (median and IQR) to describe changes in outcome data and qualitative data will be analysed following the Framework Method. ETHICS AND DISSEMINATION: This study has ethical approval from both institutions: the University of Birmingham (ERN_20-0695) and Western University (2020-116233-47832). Findings will be published in a peer-reviewed journal and disseminated to key stakeholders in musculoskeletal physical therapy education and practice.
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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 | 0.034 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".