Bibliographic record
Abstract
As a physiotherapy educator and researcher now based in New Zealand, but who previously trained and worked in Ireland over a period of twenty years, I have been afforded the indulgence and particular perspective afforded by distance in sharing some views on global developments in physiotherapy education, and its relevance for the Irish profession. Physiotherapy education in Ireland is recognised as some of the best in the world, 1 based upon a history of academic innovation (e.g. the early move of programmes to degree status), internationally benchmarked four year baccalaureate and masters degrees, and – most importantly – the calibre of graduates from the Irish Schools. In more recent years, the rapid development of high quality research programmes within the Schools has served to bolster further the image of the Irish academy within the international profession. Those who were fortunate enough to be able to attend the education sessions at last year’s World Confederation of Physical Therapy (WCPT) meeting in Vancouver, or the more recent European Congress on Physiotherapy Education in Stockholm in September of this year, would have been struck by the rapid development and changes in professional education in recent years. The sheer range and scope of education papers presented at these meetings would have been unimaginable a decade ago; by any measure, physiotherapy education, and the wider profession, have come a long way in the relatively short interval since the days of hospital-based diploma-level training programmes. 1
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 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".