Current State and Future of Teacher/Coach Education
Bibliographic record
Abstract
This article discusses current and future trends in Physical Education Teacher Education (PETE) and sports coaching. PETE exists internationally with differences in programmes and expectations across and within countries. Current PETE programmes from three countries are shared as examples, including the United States, Canada, and Brazil. International future trends in PETE programmes are discussed including (a) alternative teacher education pathways; (b) integrating occupational socialisation theory into teacher education programmes; (c) recruiting more diverse populations of PETE students; (d) preparing PETE students to work with diverse populations and in diverse contexts; and (e) preparing PETE students for expanded roles in schools. The second section of the article discusses current and future trends in training for youth sports coaches. Similar to the PETE programmes, coaching preparation programmes vary greatly across and within countries, and examples from the three countries identified earlier are shared. Future trends in coaching youth are also discussed including (a) sports organisations having a key role in creating a culture of continuous development for coaches; (b) transitions in the way coach education is carried out; and (c) multiple perspectives of coach development being supported including larger sport coaching teams. Change is coming, self-generated and from outside our fields, and it will be up to PETE and sports programmes to embrace the upcoming changes to better prepare all professionals for their important roles in youth education and leadership.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".