Bibliographic record
Abstract
It turns out I am a terrible physiotherapist. If you practise in a ‘Hands On’ manner and believe everything you read online, then you might come to the conclusion that you are too. Such is the vehemence of opinion in the ‘Hands On-Hands Off’ debate on platforms such as Twitter, it seems to echo the polarising political discourse that is now so common. Now, we know we are not terrible therapists. We know that as physiotherapists who include manual therapy in our treatment approach, we are part of a network with a long, proud history of excellence in clinical knowledge, techniques and reasoning. But lately it seems that special interest groups such as the Orthopaedic Division of the Canadian Physiotherapy Association (CPA) are facing some serious challenges. There has long been a perception that we are all about mobilisations (‘mobes’) and manipulations (‘manips’) but this has been amplified recently through social media. The potential impact of this negative rhetoric is worth considering, even if you are not active on social media yourself. Will recent graduates continue to study manual therapy-based curricula when faced with the plethora of alternative educational opportunities in conjunction with obdurate voices on social media? Similarly, in the face of the naysaying, will some therapists lose faith in their training and think twice about instructing in orthopaedic or manual therapy courses? The net result could be that therapists of all levels of training develop an ortho identity crisis. A clear professional identity is important. It guides our path and it gives us strength when facing challenges. The identity of our profession has been and continues to be evaluated in different ways. Pat Miller, Vanina Dal Bello-Haas and Chantal Lauzon are finalising the core professional values of the CPA through a Delphi process.1 Dave Walton and the Physio Moves Canada team connected …
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.219 | 0.118 |
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".