Episode 40 - Telerehabilitation Myth Busters
Bibliographic record
Abstract
Speakers: Leanne Loranger and Jody ProharIn this episode Jody and Leanne discuss the current landscape of telerehabilitation use by physiotherapists in Alberta, cover frequent questions that came up during the COVID-19 pandemic, and bust common telerehabilitation myths.Do you have questions about:Setting fees for telerehabilitation services?Expectations regarding safety and risk management?Privacy and rules for where private information is stored?Recording and documenting telerehabilitation visits?Delivering physiotherapy services in another Canadian province or territory using telerehabilitation?This episode is for you!Resources:Telerehabilitation Guide for Alberta Physiotherapists. College of Physiotherapists of Alberta. https://www.cpta.ab.ca/for-physiotherapists/resources/guides-and-guidelines/telerehabilitation-guide/Links: Subscribe on Apple PodcastsSubscribe on Google Podcasts Subscribe on Spotify
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.675 | 0.003 |
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; both teacher heads agree on what is shown here.
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".