‘Train‐the‐Trainer’: an effective and successful model to accelerate training and improve physiotherapy services for persons with haemophilia in China
Bibliographic record
Abstract
The objective of this study was to teach a small group of Chinese physiatrists and physiotherapists to: (i) become trainers and leaders in haemophilia physiotherapy (PT) care in China and (ii) to acquire rapid proficiency in using the reliable and validated Hemophilia Joint Health Score (HJHS) for evaluating musculoskeletal health in boys with haemophilia. Two experienced Canadian physiotherapists and co-developers of the HJHS moderated a 4-day PT training workshop with six Chinese participants. Emphasis was placed on instruction and practice in administering the HJHS. Practical sessions with haemophilia patients were interchanged with theory (power point presentations) and interactive question and answer periods. A proficient, knowledgeable translator was an essential component of the workshop. Upon workshop completion, the six trainees demonstrated improved haemophilia-specific PT knowledge and were fully familiar with the HJHS and its administration. The latter was assessed in a mini-reliability study. The 'Train-the-Trainer' model is a very effective education programme designed to accelerate training in haemophilia PT to meet the rapidly increasing need for haemophilia-specific rehabilitation services in a very large country such as China. It is anticipated that physiatrists/physiotherapists at newly established Chinese haemophilia treatment centres will receive training in haemophilia care as a result of this unique programme in the immediate future.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".